From 7fd8ec86eedd7c4ebba4db1809ada26c2b352776 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 29 Jan 2026 13:10:57 +0100 Subject: [PATCH 01/87] ocsort with piotr's review changes --- docs/index.md | 12 +- docs/trackers/ocsort.md | 145 +++++++++++++++++ mkdocs.yml | 1 + trackers/__init__.py | 3 +- trackers/core/ocsort/__init__.py | 0 trackers/core/ocsort/tracker.py | 261 +++++++++++++++++++++++++++++++ trackers/core/ocsort/tracklet.py | 163 +++++++++++++++++++ trackers/core/ocsort/utils.py | 189 ++++++++++++++++++++++ trackers/utils/converters.py | 45 ++++++ trackers/utils/kalman_filter.py | 150 ++++++++++++++++++ 10 files changed, 967 insertions(+), 2 deletions(-) create mode 100644 docs/trackers/ocsort.md create mode 100644 trackers/core/ocsort/__init__.py create mode 100644 trackers/core/ocsort/tracker.py create mode 100644 trackers/core/ocsort/tracklet.py create mode 100644 trackers/core/ocsort/utils.py create mode 100644 trackers/utils/converters.py create mode 100644 trackers/utils/kalman_filter.py diff --git a/docs/index.md b/docs/index.md index 8aee49df..263ae338 100644 --- a/docs/index.md +++ b/docs/index.md @@ -54,6 +54,16 @@ You can install and use `trackers` in a [**Python>=3.10**](https://www.python.or [:simple-googlecolab: Run Google Colab](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-bytetrack-tracker.ipynb) +- **How to Track Objects with OC-SORT** + + --- + + [![](url-to-image)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-ocsort-tracker.ipynb) + + End-to-end example showing how to run RF-DETR detection with the OC-SORT tracker. + + [:simple-googlecolab: Run Google Colab](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-ocsort-tracker.ipynb) + @@ -65,7 +75,7 @@ Trackers gives you clean, modular re-implementations of leading multi-object tra |:-----------:|:-------------------------------------------:|:----------:|:----------:|:----------:|:--------------:|:--------------:| | SORT | [`SORTTracker`](trackers/sort.md) | 58.4 | 69.9 | 67.2 | 70.9 | 81.6 | | ByteTrack | [`ByteTrackTracker`](trackers/bytetrack.md) | **60.1** | **73.2** | **74.1** | **73.0** | **84.0** | -| OC-SORT | `OCSORTTracker` | — | — | — | — | — | +| OC-SORT | [`OCSORTTracker`](trackers/ocsort.md) | will add them when confirmed | — | — | — | — | ## Quickstart diff --git a/docs/trackers/ocsort.md b/docs/trackers/ocsort.md new file mode 100644 index 00000000..478c21ae --- /dev/null +++ b/docs/trackers/ocsort.md @@ -0,0 +1,145 @@ +--- +comments: true +--- + +# OC-SORT + +## Overview + +OC-SORT remains Simple, Online, and Real-Time like ([SORT](../sort/tracker.md)) but improves robustness during occlusion and non-linear motion. +It recognizes limitations from SORT and the linear motion assumption of the Kalman filter, and adds three mechanisms to enhance tracking. These +mechanisms help having better Kalman Filter parameters after an occlusion, add a term to the association process to incorporate how consistent is the direction with the new association with respect to the tracks' previous direction and add a second-stage association step between the last observation of unmatched tracks and the unmatched observations after the usual association to attempt to recover tracks that were lost +due to object stopping or short-term occlusion. + +## Benchmarks + +For comparisons with other trackers, plus full details on the datasets and evaluation metrics used, see the [benchmarks](../learn/benchmarks.md) page. + +| Dataset | HOTA | IDF1 | MOTA | +|:---------:|:----:|:----:|:----:| +| MOT17 | 59.1 | 71.8 | 76.6 | +| SportsMOT | 69.9 | 69.2 | 95.0 | +| SoccerNet | 77.8 | 72.1 | 93.9 | + +## Run on video, webcam, or RTSP stream + +These examples use OpenCV for decoding and display. Replace ``, ``, and `` with your inputs. `` is usually 0 for the default camera. + +=== "Video" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import OCSORTTracker + + tracker = OCSORTTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + OC-SORT", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + +=== "Webcam" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import OCSORTTracker + + tracker = OCSORTTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open webcam") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + OC-SORT", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + +=== "RTSP" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import OCSORTTracker + + tracker = OCSORTTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open RTSP stream") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + OC-SORT", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + + +## API + +::: trackers.core.ocsort.tracker.OCSORTTracker diff --git a/mkdocs.yml b/mkdocs.yml index 764ae865..0896df9c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -107,3 +107,4 @@ nav: - Trackers: - SORT: trackers/sort.md - ByteTrack: trackers/bytetrack.md + - OCSORT: trackers/ocsort.md diff --git a/trackers/__init__.py b/trackers/__init__.py index a230da3a..7697d2dd 100644 --- a/trackers/__init__.py +++ b/trackers/__init__.py @@ -1,4 +1,5 @@ from trackers.core.bytetrack.tracker import ByteTrackTracker from trackers.core.sort.tracker import SORTTracker +from trackers.core.ocsort.tracker import OCSORTTracker -__all__ = ["ByteTrackTracker", "SORTTracker"] +__all__ = ["ByteTrackTracker", "SORTTracker", "OCSORTTracker"] diff --git a/trackers/core/ocsort/__init__.py b/trackers/core/ocsort/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py new file mode 100644 index 00000000..0b5479b2 --- /dev/null +++ b/trackers/core/ocsort/tracker.py @@ -0,0 +1,261 @@ +import numpy as np +import supervision as sv +from scipy.optimize import linear_sum_assignment + +from trackers.core.base import BaseTracker +from trackers.core.ocsort.tracklet import OCSORTTracklet +from trackers.core.ocsort.utils import ( + add_track_id_detections, + build_direction_consistency_matrix, + get_iou_matrix, + get_iou_matrix_between_boxes, +) + + +class OCSORTTracker(BaseTracker): + """Implements OC-SORT (Observation Centric Simple Online and Realtime Tracking). + + OC-SORT remains Simple, Online, and Real-Time but improves robustness during occlusion and non-linear motion. + It recognizes limitations from SORT and the linear motion assumption of the Kalman filter, and adds three + mechanisms to enhance tracking. The first mechanism is Observation-Centre Re-Update (ORU), which runs a + predict-update loop with a 'virtual trajectory' depending on the last observation and new observation when + a track is re-activated after being lost. The second mechanism is Observation-Centric Momentum (OCM), that + incorporates the direction consistency of tracks in the cost matrix for the association. Finally, OC-SORT adds + Observation-centric Recovery (OCR), a second-stage association step between the last observation of unmatched + tracks to the unmatched observations after the usual association. It attempts to recover tracks that were lost + due to object stopping or short-term occlusion. + Args: + lost_track_buffer: Number of frames to buffer when a track is lost. + Increasing lost_track_buffer enhances occlusion handling, significantly + improving tracking through occlusions, but may increase the possibility + of ID switching for objects with similar appearance. + frame_rate: Frame rate of the video (frames per second). + Used to calculate the maximum time a track can be lost. + minimum_consecutive_frames: Number of consecutive frames that an object + must be tracked before it is considered a 'valid' track. Increasing + `minimum_consecutive_frames` prevents the creation of accidental tracks + from false detection or double detection, but risks missing shorter + tracks. Before the tracklet is considered valid, it will be assigned + `-1` as its `tracker_id`. + minimum_iou_threshold: IOU threshold for associating detections to + existing tracks. + direction_consistency_weight: Weight for inertia term in association cost. Higher values give more importance + to the angle difference between the motion direction and the association direction. + high_conf_det_threshold: Confidence threshold to consider a detection as high confidence. If a detection has + confidence lower than this threshold, it will not be considered for association. + + """ # noqa: E501 + + count_id: int = 0 + + def __init__( + self, + lost_track_buffer: int = 30, + frame_rate: float = 30.0, + minimum_consecutive_frames: int = 3, # should change this for min_hits? + minimum_iou_threshold: float = 0.3, + direction_consistency_weight: float = 0.2, + high_conf_det_threshold: float = 0.6, + ) -> None: + # Calculate maximum frames without update based on lost_track_buffer and + # frame_rate. This scales the buffer based on the frame rate to ensure + # consistent time-based tracking across different frame rates. + self.maximum_frames_without_update = int(frame_rate / 30.0 * lost_track_buffer) + self.minimum_consecutive_frames = minimum_consecutive_frames + self.minimum_iou_threshold = minimum_iou_threshold + self.direction_consistency_weight = direction_consistency_weight + self.high_conf_det_threshold = high_conf_det_threshold + # Active tracks + self.tracks: list[OCSORTTracklet] = [] + + def _get_associated_indices( + self, + iou_matrix: np.ndarray, + direction_consistency_matrix: np.ndarray, + ) -> tuple[list[tuple[int, int]], list[int], list[int]]: + """ + Associate detections to tracks based on IOU + + Args: + iou_matrix: IOU cost matrix. + direction_consistency_matrix: Direction of the tracklet consistency cost matrix. + + Returns: + tuple[list[tuple[int, int]], list[int], list[int]]: Matched indices, + unmatched tracks, unmatched detections. + """ # noqa: E501 + matched_indices = [] + n_tracks, n_detections = iou_matrix.shape + unmatched_tracks = set(range(n_tracks)) + unmatched_detections = set(range(n_detections)) + if n_tracks > 0 and n_detections > 0: + # Find optimal assignment using scipy.optimize.linear_sum_assignment. + cost_matrix = ( + iou_matrix + + self.direction_consistency_weight * direction_consistency_matrix + ) + row_indices, col_indices = linear_sum_assignment(cost_matrix, maximize=True) + for row, col in zip(row_indices, col_indices): + if iou_matrix[row, col] >= self.minimum_iou_threshold: + matched_indices.append((row, col)) + unmatched_tracks.remove(row) + unmatched_detections.remove(col) + + return ( + matched_indices, + sorted(list(unmatched_tracks)), + sorted(list(unmatched_detections)), + ) + + def _spawn_new_tracklets( + self, + detections: sv.Detections, + unmatched_detections: list[int], + ) -> None: + """ + Create new tracklets only for unmatched detections with confidence + above threshold. + + Args: + detections: The latest set of object detections. + detection_boxes: Detected bounding boxes in the + form [x1, y1, x2, y2]. + """ + for detection_idx in unmatched_detections: + new_tracker = OCSORTTracklet(detections.xyxy[detection_idx]) + self.tracks.append(new_tracker) + + def update(self, detections: sv.Detections) -> sv.Detections: + """Updates the tracker state with new detections. + + Performs Kalman filter prediction, associates detections with existing + tracklets based on IOU, updates matched tracklets, and initializes new + tracklets for unmatched high-confidence detections. + + Args: + detections: The latest set of object detections from a frame. + + Returns: + sv.Detections: A copy of the input detections, augmented with assigned + `tracklet_id` for each successfully tracked object. Detections not + associated with a track will not have a `tracklet_id`. + """ # noqa: E501 + + if len(self.tracks) == 0 and len(detections) == 0: + detections.tracker_id = np.array([], dtype=int) + return detections + + detections = detections[detections.confidence >= self.high_conf_det_threshold] + + updated_detections: list[ + sv.Detections + ] = [] # List for returning the updated detections + # Convert detections to a (N x 4) array (x1, y1, x2, y2) + detection_boxes = ( + detections.xyxy if len(detections) > 0 else np.array([]).reshape(0, 4) + ) + + # Predict new locations for existing tracks KF + for tracker in self.tracks: + tracker.predict() + + # Build IOU cost matrix between detections and predicted bounding boxes + iou_matrix = get_iou_matrix(self.tracks, detection_boxes) + + direction_consistency_matrix = build_direction_consistency_matrix( + self.tracks, detection_boxes + ) + direction_consistency_matrix *= detections.confidence[np.newaxis, :] + + # 1st Association of detections to tracks (OCM) + matched_indices, unmatched_tracks, unmatched_detections = ( + self._get_associated_indices(iou_matrix, direction_consistency_matrix) + ) + + # Update matched trackers with assigned detections + for row, col in matched_indices: + self.tracks[row].update(detection_boxes[col]) + add_track_id_detections( + self.tracks[row], detections[col : col + 1], updated_detections + ) + + # Run 2nd Chance Association (OCR) + # between the last observation of unmatched tracks to the unmatched observations #noqa: E501 + if len(unmatched_detections) > 0 and len(unmatched_tracks) > 0: + last_observation_of_tracks = np.array( + [self.tracks[t_id].last_observation for t_id in unmatched_tracks] + ) + + ocr_iou_matrix = get_iou_matrix_between_boxes( + last_observation_of_tracks, detection_boxes[unmatched_detections] + ) + + ocr_matched_indices, ocr_unmatched_tracks, ocr_unmatched_detections = ( + self._get_associated_indices( + ocr_iou_matrix, + np.zeros_like(ocr_iou_matrix), + ) + ) + + for ocr_row, ocr_col in ocr_matched_indices: + track_idx = unmatched_tracks[ocr_row] + det_idx = unmatched_detections[ocr_col] + self.tracks[track_idx].update(detection_boxes[det_idx]) + add_track_id_detections( + self.tracks[track_idx], + detections[det_idx : det_idx + 1], + updated_detections, + ) + + self.tracks = self.activate_or_kill_tracklets() + self._spawn_new_tracklets( + detections[unmatched_detections], ocr_unmatched_detections + ) + left_detections = detections[ocr_unmatched_detections] + left_detections.tracker_id = np.array( + [-1] * len(left_detections), dtype=int + ) + updated_detections.append(left_detections) + + else: + self.tracks = self.activate_or_kill_tracklets() + self._spawn_new_tracklets(detections, unmatched_detections) + left_detections = detections[unmatched_detections] + left_detections.tracker_id = np.array( + [-1] * len(left_detections), dtype=int + ) + updated_detections.append(left_detections) + final_updated_detections = sv.Detections.merge(updated_detections) + if len(final_updated_detections) == 0: + final_updated_detections.tracker_id = np.array([], dtype=int) + return final_updated_detections + + def reset(self) -> None: + """Resets the tracker's internal state. + + Clears all active tracks and resets the track ID counter. + """ + self.tracks = [] + OCSORTTracklet.count_id = 0 + + def activate_or_kill_tracklets(self): + """Activates or kills tracklets based on their status. + + This method checks each tracklet's status and either activates it + (assigning a tracker ID) if it meets the criteria for being a valid + track, or kills it (removing it from active tracking) if it has been + lost for too long. + """ + alive_tracklets = [] + for tracklet in self.tracks: + is_mature = ( + tracklet.number_of_successful_consecutive_updates + >= self.minimum_consecutive_frames + ) + # is_active = tracklet.time_since_update == 0 + if tracklet.time_since_update <= self.maximum_frames_without_update: + alive_tracklets.append(tracklet) + + if is_mature and tracklet.tracker_id == -1: + tracklet.tracker_id = OCSORTTracklet.get_next_tracker_id() + return alive_tracklets \ No newline at end of file diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py new file mode 100644 index 00000000..d1c3ebe4 --- /dev/null +++ b/trackers/core/ocsort/tracklet.py @@ -0,0 +1,163 @@ +import numpy as np +from supervision import xyxy_to_xywh + +from trackers.utils.converters import ( + xcycsr_to_xyxy, + xyxy_to_xcycsr, +) +from trackers.utils.kalman_filter import KalmanFilter + + +class OCSORTTracklet: + """ + The `OCSORTTracklet` class represents the internals of a single + tracked object (bounding box), with a Kalman filter to predict and update + its position. + Attributes: + age: Age of the tracklet in frames. + kalman_filter: The Kalman filter instance for this tracklet. + tracker_id: Unique identifier for the tracker. + state_transition_matrix: State transition matrix for the Kalman filter. (referred as F) + number_of_successful_consecutive_updates: Number of times the object has been + updated successfully in a row. + time_since_update: Number of frames since the last update. + last_observation: The last observed bounding box. + previous_to_last_observation: The bounding box observed before the last one. + kalman_filter_state_before_being_lost: The Kalman filter state before the tracklet was lost. + kalman_filter_parameters_before_being_lost: The Kalman filter parameters before the tracklet was lost. + """ # noqa: E501 + + count_id: int = 0 + + def __init__(self, initial_bbox) -> None: + self.age = 0 + # state format: (x, y, s, r, vx, vy, vs). As detailed in SORT paper, r is the aspect ratio and constant! # noqa: E501 + self.kalman_filter = KalmanFilter( + bbox=xyxy_to_xcycsr(initial_bbox), + state_dim=7, + state_transition_matrix=np.array( + [ + [1, 0, 0, 0, 1, 0, 0], + [0, 1, 0, 0, 0, 1, 0], + [0, 0, 1, 0, 0, 0, 1], + [0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 0, 1], + ] + ), + ) + + self.kalman_filter.R[2:, 2:] *= 10.0 + self.kalman_filter.P[4:, 4:] *= ( + 1000.0 # give high uncertainty to the unobservable initial velocities + ) + self.kalman_filter.P *= 10.0 + self.kalman_filter.Q[-1, -1] *= 0.01 + self.kalman_filter.Q[4:, 4:] *= 0.01 + self.last_observation = initial_bbox # None + self.previous_to_last_observation = None # For velocity of track + + # Will be assigned a real ID when the track is considered mature + self.tracker_id = -1 + + # Number of hits indicates how many times the object has been + # updated successfully + self.number_of_successful_consecutive_updates = 1 + # Number of frames since the last update + self.time_since_update = 0 + self.save_kalman_filter_state() + + def save_kalman_filter_state(self) -> None: + """Saves the current Kalman filter state and parameters.""" + self.kalman_filter_state_before_being_lost = self.kalman_filter.state.copy() + self.kalman_filter_parameters_before_being_lost = { + "H": self.kalman_filter.H.copy(), + "Q": self.kalman_filter.Q.copy(), + "R": self.kalman_filter.R.copy(), + "P": self.kalman_filter.P.copy(), + } + + @classmethod + def get_next_tracker_id(cls) -> int: + next_id = cls.count_id + cls.count_id += 1 + return next_id + + def update(self, bbox: np.ndarray) -> None: + """Updates the tracklet with a new bounding box observation. + + Args: + bbox (np.ndarray): The new bounding box in the form [x1, y1, x2, y2]. + """ + if self.is_lost(): + self.re_update(bbox) + else: + self.kalman_filter.update(xyxy_to_xcycsr(bbox)) + # save the last before being lost KF parameters for re-updating + self.save_kalman_filter_state() + self.time_since_update = 0 + self.number_of_successful_consecutive_updates += 1 + self.previous_to_last_observation = self.last_observation + self.last_observation = bbox + + def predict(self) -> np.ndarray: + """Predicts the next bounding box position using the Kalman filter. + + Returns: + np.ndarray: The predicted bounding box in the form [x1, y1, x2, y2]. + """ + self.kalman_filter.predict() + self.age += 1 + if self.time_since_update > 0: + self.number_of_successful_consecutive_updates = 0 + + self.time_since_update += 1 + predicted_bbox = self.kalman_filter.get_state_bbox() + return xcycsr_to_xyxy(predicted_bbox) + + def is_lost( + self, + ) -> bool: + """Determines if the tracklet is considered lost.""" + return self.time_since_update > 1 + + def re_update(self, bbox: np.ndarray) -> None: + """Re-updates the tracklet with the virtual trajectory generated out of the line that joins + the last_observation and the parameter bbox. + + Args: + bbox: The new bounding box in the form [x1, y1, x2, y2]. + """ # noqa: E501 + self.kalman_filter.state = self.kalman_filter_state_before_being_lost + self.kalman_filter.set_parameters( + **self.kalman_filter_parameters_before_being_lost + ) + bbox_xywh = xyxy_to_xywh(np.array([bbox]))[0] + last_observation_xywh = xyxy_to_xywh(np.array([self.last_observation]))[0] + for i in range(1, self.time_since_update + 1): + # Interpolate linearly between last_observation and bbox + virtual_bbox_xywh = last_observation_xywh + ( + bbox_xywh - last_observation_xywh + ) * (i / (self.time_since_update)) + virtual_bbox_xysa = np.copy(virtual_bbox_xywh) + s = virtual_bbox_xywh[2] * virtual_bbox_xywh[3] # w*h + virtual_bbox_xysa[2] = s + virtual_bbox_xysa[3] = ( + virtual_bbox_xywh[2] / virtual_bbox_xywh[3] + ) # w/h = r + + self.kalman_filter.predict() + self.kalman_filter.update(virtual_bbox_xysa) + + self.previous_to_last_observation = self.last_observation + self.last_observation = bbox + self.time_since_update = 0 + + def get_state_bbox(self) -> np.ndarray: + """Returns the current bounding box estimate from the Kalman filter. + + Returns: + np.ndarray: The current bounding box in the form [x1, y1, x2, y2]. + """ + return xcycsr_to_xyxy(self.kalman_filter.get_state_bbox()) \ No newline at end of file diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py new file mode 100644 index 00000000..eb93a53d --- /dev/null +++ b/trackers/core/ocsort/utils.py @@ -0,0 +1,189 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copied from OC-SORT https://github.com/noahcao/OC_SORT/ +# Licensed under the MIT License [see LICENSE for details] +# ------------------------------------------------------------------------ + +from copy import deepcopy +from typing import Sequence, cast + +import numpy as np +import supervision as sv + +from trackers.core.ocsort.tracklet import OCSORTTracklet + + +def speed_direction(bbox1, bbox2): # from oc sort repo + cx1, cy1 = (bbox1[0] + bbox1[2]) / 2.0, (bbox1[1] + bbox1[3]) / 2.0 + cx2, cy2 = (bbox2[0] + bbox2[2]) / 2.0, (bbox2[1] + bbox2[3]) / 2.0 + speed = np.array([cy2 - cy1, cx2 - cx1]) + norm = np.sqrt((cy2 - cy1) ** 2 + (cx2 - cx1) ** 2) + 1e-6 + return speed / norm + + +def build_direction_consistency_matrix( + # could we try to vectorize this? yes, lets compare both methods later + tracklets: list[OCSORTTracklet], + detection_boxes: np.ndarray, +) -> np.ndarray: + n_tracklets = len(tracklets) + n_detections = detection_boxes.shape[0] + direction_consistency_matrix = np.zeros( + (n_tracklets, n_detections), dtype=np.float32 + ) + + for t, tracklet in enumerate(tracklets): + if tracklet.previous_to_last_observation is None: # if there is no previous box + continue + # In case the track is lost we use the last known box + last_observation = tracklet.last_observation + tracklet_speed = speed_direction( + tracklet.previous_to_last_observation, last_observation + ) + + for d in range(n_detections): + detection_box = detection_boxes[d] + association_speed = speed_direction(last_observation, detection_box) + + # Compute cosine similarity + cos_sim = np.dot(tracklet_speed, association_speed) + direction_consistency_matrix[t, d] = cos_sim + + return direction_consistency_matrix + + +def speed_direction_batch(dets, tracks): # From oc sort repo + """Function for calculating direction between detections and tracks in a batch way. + + Args: + dets: Detected bounding boxes in the form [x1, y1, x2, y2]. + tracks: Tracked bounding boxes in the form [x1, y1, x2, y2].""" + tracks = tracks[..., np.newaxis] + CX1, CY1 = (dets[:, 0] + dets[:, 2]) / 2.0, (dets[:, 1] + dets[:, 3]) / 2.0 + CX2, CY2 = (tracks[:, 0] + tracks[:, 2]) / 2.0, (tracks[:, 1] + tracks[:, 3]) / 2.0 + dx = CX1 - CX2 + dy = CY1 - CY2 + norm = np.sqrt(dx**2 + dy**2) + 1e-6 + dx = dx / norm + dy = dy / norm + return dy, dx # size: num_track x num_det + + +def build_direction_consistency_matrix_batch( + # vectorized version from oc sort repo but adapted to our tracklet class, still needs testing and is not currently used + tracklets: list[OCSORTTracklet], + detection_boxes: np.ndarray, +) -> np.ndarray: + velocities = np.array( + [ + speed_direction( + tracklet.previous_to_last_observation, tracklet.last_observation + ) + if tracklet.previous_to_last_observation is not None + else (0, 0) + for tracklet in tracklets + ] + ) + Y, X = speed_direction_batch(detection_boxes, tracklets) # these will break + inertia_Y, inertia_X = velocities[:, 0], velocities[:, 1] + inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1) + inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1) + diff_angle_cos = inertia_X * X + inertia_Y * Y + diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1) + diff_angle = np.arccos(diff_angle_cos) + diff_angle = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi + + valid_mask = np.ones(len(tracklets)) + # valid_mask[np.where(previous_obs[:,4]<0)] = 0 + previous_to_last_observation_is_none = np.array( + [tracklet.previous_to_last_observation is None for tracklet in tracklets] + ) + valid_mask[previous_to_last_observation_is_none] = 0 + valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1) + + angle_diff_cost = valid_mask * diff_angle + angle_diff_cost = angle_diff_cost.T # check if this transpose is needed + return angle_diff_cost + + +def add_track_id_detections( + track: OCSORTTracklet, + detection: sv.Detections, + updated_detections: list[sv.Detections], +) -> sv.Detections: + """ + The function prepares the updated Detections with track IDs. + If a tracker is "mature" (>= `minimum_consecutive_frames`) or recently updated, + it is assigned an ID to the detection that just updated it. + + Args: + detections: The latest set of object detections. + tracklets: List of OCSORTTracklet objects. + updated_detections: List of detections in which we add detections with assigned track IDs. + Returns: + sv.Detections: A copy of the detections with `tracker_id` set + for each detection that is tracked. + """ # noqa: E501 + new_det = deepcopy(detection) + # Add cast to clarify type for mypy + new_det = cast(sv.Detections, new_det) # ADDED cast + new_det.tracker_id = np.array([track.tracker_id]) + updated_detections.append(new_det) + + +def get_iou_matrix( + trackers: Sequence[OCSORTTracklet], detection_boxes: np.ndarray +) -> np.ndarray: + """ + Build IOU cost matrix between detections and predicted bounding boxes + + Args: + detection_boxes: Detected bounding boxes in the + form [x1, y1, x2, y2]. + + Returns: + np.ndarray: IOU cost matrix. + """ + predicted_boxes = np.array([t.get_state_bbox() for t in trackers]) + if len(predicted_boxes) == 0 and len(trackers) > 0: + # Handle case where get_state_bbox might return empty array + predicted_boxes = np.zeros((len(trackers), 4), dtype=np.float32) + + if len(trackers) > 0 and len(detection_boxes) > 0: + iou_matrix = sv.box_iou_batch(predicted_boxes, detection_boxes) + else: + iou_matrix = np.zeros((len(trackers), len(detection_boxes)), dtype=np.float32) + + return iou_matrix + + +def get_iou_matrix_between_boxes( + last_observations: np.ndarray, detection_boxes: np.ndarray +) -> np.ndarray: + """ + Build IOU cost matrix between detections and predicted bounding boxes + + Args: + last_observations: Last observed bounding boxes of tracks in the + form [x1, y1, x2, y2]. + detection_boxes: Detected bounding boxes in the + form [x1, y1, x2, y2]. + + Returns: + np.ndarray: IOU cost matrix. + """ + if len(last_observations) == 0 and len(last_observations) > 0: + # Handle case where get_state_bbox might return empty array + last_observations = np.zeros((len(last_observations), 4), dtype=np.float32) + + if len(last_observations) > 0 and len(detection_boxes) > 0: + iou_matrix = sv.box_iou_batch(last_observations, detection_boxes) + else: + iou_matrix = np.zeros( + (len(last_observations), len(detection_boxes)), dtype=np.float32 + ) + + return iou_matrix \ No newline at end of file diff --git a/trackers/utils/converters.py b/trackers/utils/converters.py new file mode 100644 index 00000000..da0ad4be --- /dev/null +++ b/trackers/utils/converters.py @@ -0,0 +1,45 @@ +import numpy as np + + +def xyxy_to_xcycsr(xyxy: np.ndarray) -> np.ndarray: + """ + Converts bounding box coordinates from `(x_min, y_min, x_max, y_max)` + format to `(x_center, y_center, scale, aspect_ratio)` format. + + Args: + xyxy: A numpy array of shape `(4)` which corresponds to a bounding box + in the format `(x_min, y_min, x_max, y_max)`. + + Returns: + A numpy array of shape `(4)` that corresponds to a bounding box + in the format `(x_min, y_min, x_max, y_max)`. + """ + w = xyxy[2] - xyxy[0] + h = xyxy[3] - xyxy[1] + x = xyxy[0] + w / 2.0 + y = xyxy[1] + h / 2.0 + s = w * h + r = w / float(h + 1e-6) + return np.array([x, y, s, r]) + + +def xcycsr_to_xyxy(xcycsr: np.ndarray) -> np.ndarray: + """ + Takes a bounding box in the centre form [x,y,s,r] and returns it in the form + [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right + Args: + xcycsr: A numpy array of shape `(4)` which corresponds to a centered bounding + box in the form [x,y,s,r] + Returns: + np.ndarray: bounding box in the form [x1,y1,x2,y2] + """ + w = np.sqrt(xcycsr[2] * xcycsr[3]) + h = xcycsr[2] / w + return np.array( + [ + xcycsr[0] - w / 2.0, + xcycsr[1] - h / 2.0, + xcycsr[0] + w / 2.0, + xcycsr[1] + h / 2.0, + ] + ) \ No newline at end of file diff --git a/trackers/utils/kalman_filter.py b/trackers/utils/kalman_filter.py new file mode 100644 index 00000000..35b6324f --- /dev/null +++ b/trackers/utils/kalman_filter.py @@ -0,0 +1,150 @@ +import numpy as np +from numpy.typing import NDArray + + +class KalmanFilter: + """ + The `OCSORTKalmanBoxTracker` class represents the internals of a single + tracked object (bounding box), with a Kalman filter to predict and update + its position. + + Attributes: + tracker_id (int): Unique identifier for the tracker. + number_of_successful_updates (int): Number of times the object has been + updated successfully. + time_since_update (int): Number of frames since the last update. + state: State vector of the bounding box. + F: State transition matrix. + H: Measurement matrix. + Q: Process noise covariance matrix. + R: Measurement noise covariance matrix. + P: Error covariance matrix. + + Args: + bbox: Initial bounding box in the form [x1, y1, x2, y2]. + state_dim (int): Dimension of the state vector. Default is 7 using (x, y, s, r, vx, vy, vs). + state_transition_matrix: State transition matrix F. Default is identity. + """ # noqa: E501 + + state: NDArray[np.float32] + F: NDArray[np.float32] + H: NDArray[np.float32] + Q: NDArray[np.float32] + R: NDArray[np.float32] + P: NDArray[np.float32] + + def __init__( + self, + bbox: NDArray[np.float64], + state_dim: int = 7, + state_transition_matrix: np.ndarray = np.eye(7), + ) -> None: + # Number of hits indicates how many times the object has been + # updated successfully + self.number_of_successful_updates = 1 + # Number of frames since the last update + self.time_since_update = 0 + self.state_dim = state_dim + # For simplicity, we keep a small state vector: + # We'll store the bounding box in "self.state" + self.state = np.zeros((state_dim, 1), dtype=np.float32) + self.F = state_transition_matrix.astype(np.float32) + # Initialize state directly from the first detection + bbox_float: NDArray[np.float32] = bbox.astype(np.float32) + self.state[0, 0] = bbox_float[0] + self.state[1, 0] = bbox_float[1] + self.state[2, 0] = bbox_float[2] + self.state[3, 0] = bbox_float[3] + + # Basic constant velocity model + self._initialize_kalman_filter() + + def _initialize_kalman_filter(self) -> None: + """ + Sets up the matrices for the Kalman filter. + """ + # We assume a constant velocity model. Positions are incremented by + # velocity each step. + + # Measurement matrix (H) + self.H = np.eye(4, self.state_dim, dtype=np.float32) # 4xself.state_dim + + # Process covariance matrix (Q) + self.Q = np.eye(self.state_dim, dtype=np.float32) # * 0.01 + + # Measurement covariance (R): noise in detection + self.R = np.eye(4, dtype=np.float32) # * 0.1 + + # Error covariance matrix (P) + self.P = np.eye(self.state_dim, dtype=np.float32) + + def predict(self) -> None: + """ + Predict the next state of the bounding box (applies the state transition). + """ + # Predict state + self.state = (self.F @ self.state).astype(np.float32) + # Predict error covariance + self.P = (self.F @ self.P @ self.F.T + self.Q).astype(np.float32) + + # Increase time since update + self.time_since_update += 1 + + def update(self, bbox: NDArray[np.float64]) -> None: + """ + Updates the state with a new detected bounding box. + + Args: + bbox: Detected bounding box in the form [x1, y1, x2, y2]. + """ + # self.time_since_update = 0 + # self.number_of_successful_updates += 1 + + # Kalman Gain + S: NDArray[np.float32] = self.H @ self.P @ self.H.T + self.R + K: NDArray[np.float32] = (self.P @ self.H.T @ np.linalg.inv(S)).astype( + np.float32 + ) + + # Residual + measurement: NDArray[np.float32] = bbox.reshape((4, 1)).astype(np.float32) + y: NDArray[np.float32] = ( + measurement - self.H @ self.state + ) # y should be float32 (4,1) + + # Update state + self.state = (self.state + K @ y).astype(np.float32) + + # Update covariance + identity_matrix: NDArray[np.float32] = np.eye(self.state_dim, dtype=np.float32) + self.P = ((identity_matrix - K @ self.H) @ self.P).astype(np.float32) + + def set_parameters( + self, + H: NDArray[np.float32], + Q: NDArray[np.float32], + R: NDArray[np.float32], + P: NDArray[np.float32], + ) -> None: + """ + Sets the Kalman filter parameters. + + Args: + H: Measurement matrix. + Q: Process noise covariance matrix. + R: Measurement noise covariance matrix. + P: Error covariance matrix. + """ + self.H = H + self.Q = Q + self.R = R + self.P = P + + def get_state_bbox(self) -> NDArray[np.float32]: + """ + Returns the current bounding box estimate from the state vector. + + Returns: + np.ndarray: The bounding box [x1, y1, x2, y2] + """ + return self.state[:4, 0].flatten().astype(np.float32) \ No newline at end of file From 01cd73df0eae1bc51295003971f2bfaf2142f0da Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 29 Jan 2026 12:38:40 +0000 Subject: [PATCH 02/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- trackers/__init__.py | 4 ++-- trackers/core/ocsort/tracker.py | 4 ++-- trackers/core/ocsort/tracklet.py | 2 +- trackers/core/ocsort/utils.py | 2 +- trackers/utils/converters.py | 2 +- trackers/utils/kalman_filter.py | 2 +- 6 files changed, 8 insertions(+), 8 deletions(-) diff --git a/trackers/__init__.py b/trackers/__init__.py index 7697d2dd..99182062 100644 --- a/trackers/__init__.py +++ b/trackers/__init__.py @@ -1,5 +1,5 @@ from trackers.core.bytetrack.tracker import ByteTrackTracker -from trackers.core.sort.tracker import SORTTracker from trackers.core.ocsort.tracker import OCSORTTracker +from trackers.core.sort.tracker import SORTTracker -__all__ = ["ByteTrackTracker", "SORTTracker", "OCSORTTracker"] +__all__ = ["ByteTrackTracker", "OCSORTTracker", "SORTTracker"] diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 0b5479b2..3e557c59 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -139,7 +139,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: sv.Detections: A copy of the input detections, augmented with assigned `tracklet_id` for each successfully tracked object. Detections not associated with a track will not have a `tracklet_id`. - """ # noqa: E501 + """ if len(self.tracks) == 0 and len(detections) == 0: detections.tracker_id = np.array([], dtype=int) @@ -258,4 +258,4 @@ def activate_or_kill_tracklets(self): if is_mature and tracklet.tracker_id == -1: tracklet.tracker_id = OCSORTTracklet.get_next_tracker_id() - return alive_tracklets \ No newline at end of file + return alive_tracklets diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index d1c3ebe4..8f61200e 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -160,4 +160,4 @@ def get_state_bbox(self) -> np.ndarray: Returns: np.ndarray: The current bounding box in the form [x1, y1, x2, y2]. """ - return xcycsr_to_xyxy(self.kalman_filter.get_state_bbox()) \ No newline at end of file + return xcycsr_to_xyxy(self.kalman_filter.get_state_bbox()) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index eb93a53d..35caf68c 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -186,4 +186,4 @@ def get_iou_matrix_between_boxes( (len(last_observations), len(detection_boxes)), dtype=np.float32 ) - return iou_matrix \ No newline at end of file + return iou_matrix diff --git a/trackers/utils/converters.py b/trackers/utils/converters.py index da0ad4be..b1140cd4 100644 --- a/trackers/utils/converters.py +++ b/trackers/utils/converters.py @@ -42,4 +42,4 @@ def xcycsr_to_xyxy(xcycsr: np.ndarray) -> np.ndarray: xcycsr[0] + w / 2.0, xcycsr[1] + h / 2.0, ] - ) \ No newline at end of file + ) diff --git a/trackers/utils/kalman_filter.py b/trackers/utils/kalman_filter.py index 35b6324f..3f8bf4d5 100644 --- a/trackers/utils/kalman_filter.py +++ b/trackers/utils/kalman_filter.py @@ -147,4 +147,4 @@ def get_state_bbox(self) -> NDArray[np.float32]: Returns: np.ndarray: The bounding box [x1, y1, x2, y2] """ - return self.state[:4, 0].flatten().astype(np.float32) \ No newline at end of file + return self.state[:4, 0].flatten().astype(np.float32) From 481de00bd3389ff778715454d4afca8e7c55ec7a Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 29 Jan 2026 13:43:06 +0100 Subject: [PATCH 03/87] trackers/core/ocsort --- trackers/utils/converters.py | 2 +- trackers/utils/kalman_filter.py | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/trackers/utils/converters.py b/trackers/utils/converters.py index da0ad4be..b1140cd4 100644 --- a/trackers/utils/converters.py +++ b/trackers/utils/converters.py @@ -42,4 +42,4 @@ def xcycsr_to_xyxy(xcycsr: np.ndarray) -> np.ndarray: xcycsr[0] + w / 2.0, xcycsr[1] + h / 2.0, ] - ) \ No newline at end of file + ) diff --git a/trackers/utils/kalman_filter.py b/trackers/utils/kalman_filter.py index 35b6324f..792dfc93 100644 --- a/trackers/utils/kalman_filter.py +++ b/trackers/utils/kalman_filter.py @@ -9,10 +9,10 @@ class KalmanFilter: its position. Attributes: - tracker_id (int): Unique identifier for the tracker. - number_of_successful_updates (int): Number of times the object has been + tracker_id: Unique identifier for the tracker. + number_of_successful_updates: Number of times the object has been updated successfully. - time_since_update (int): Number of frames since the last update. + time_since_update: Number of frames since the last update. state: State vector of the bounding box. F: State transition matrix. H: Measurement matrix. @@ -22,7 +22,7 @@ class KalmanFilter: Args: bbox: Initial bounding box in the form [x1, y1, x2, y2]. - state_dim (int): Dimension of the state vector. Default is 7 using (x, y, s, r, vx, vy, vs). + state_dim: Dimension of the state vector. Default is 7 using (x, y, s, r, vx, vy, vs). state_transition_matrix: State transition matrix F. Default is identity. """ # noqa: E501 @@ -147,4 +147,4 @@ def get_state_bbox(self) -> NDArray[np.float32]: Returns: np.ndarray: The bounding box [x1, y1, x2, y2] """ - return self.state[:4, 0].flatten().astype(np.float32) \ No newline at end of file + return self.state[:4, 0].flatten().astype(np.float32) From 3a9062827e9a9d120b1b07d9ee0bc59c7be6ef9b Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 29 Jan 2026 13:44:59 +0100 Subject: [PATCH 04/87] fixed pre commit --- trackers/core/ocsort/tracker.py | 6 +++--- trackers/core/ocsort/tracklet.py | 2 +- trackers/core/ocsort/utils.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 0b5479b2..35b85047 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -139,7 +139,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: sv.Detections: A copy of the input detections, augmented with assigned `tracklet_id` for each successfully tracked object. Detections not associated with a track will not have a `tracklet_id`. - """ # noqa: E501 + """ if len(self.tracks) == 0 and len(detections) == 0: detections.tracker_id = np.array([], dtype=int) @@ -190,7 +190,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: last_observation_of_tracks, detection_boxes[unmatched_detections] ) - ocr_matched_indices, ocr_unmatched_tracks, ocr_unmatched_detections = ( + ocr_matched_indices, _ocr_unmatched_tracks, ocr_unmatched_detections = ( self._get_associated_indices( ocr_iou_matrix, np.zeros_like(ocr_iou_matrix), @@ -258,4 +258,4 @@ def activate_or_kill_tracklets(self): if is_mature and tracklet.tracker_id == -1: tracklet.tracker_id = OCSORTTracklet.get_next_tracker_id() - return alive_tracklets \ No newline at end of file + return alive_tracklets diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index d1c3ebe4..8f61200e 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -160,4 +160,4 @@ def get_state_bbox(self) -> np.ndarray: Returns: np.ndarray: The current bounding box in the form [x1, y1, x2, y2]. """ - return xcycsr_to_xyxy(self.kalman_filter.get_state_bbox()) \ No newline at end of file + return xcycsr_to_xyxy(self.kalman_filter.get_state_bbox()) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index eb93a53d..df330821 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -73,7 +73,7 @@ def speed_direction_batch(dets, tracks): # From oc sort repo def build_direction_consistency_matrix_batch( - # vectorized version from oc sort repo but adapted to our tracklet class, still needs testing and is not currently used + # vectorized version from oc sort repo but adapted to our tracklet class, still needs testing and is not currently used# noqa: E501 tracklets: list[OCSORTTracklet], detection_boxes: np.ndarray, ) -> np.ndarray: @@ -186,4 +186,4 @@ def get_iou_matrix_between_boxes( (len(last_observations), len(detection_boxes)), dtype=np.float32 ) - return iou_matrix \ No newline at end of file + return iou_matrix From 81203f77424bce3047a8ae6336f38abb5b9b5b33 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 29 Jan 2026 13:45:20 +0100 Subject: [PATCH 05/87] fixed pre commit --- trackers/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/trackers/__init__.py b/trackers/__init__.py index 7697d2dd..99182062 100644 --- a/trackers/__init__.py +++ b/trackers/__init__.py @@ -1,5 +1,5 @@ from trackers.core.bytetrack.tracker import ByteTrackTracker -from trackers.core.sort.tracker import SORTTracker from trackers.core.ocsort.tracker import OCSORTTracker +from trackers.core.sort.tracker import SORTTracker -__all__ = ["ByteTrackTracker", "SORTTracker", "OCSORTTracker"] +__all__ = ["ByteTrackTracker", "OCSORTTracker", "SORTTracker"] From 56bd50be88b5e06af1ecd20b88f61d3e48ed5149 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 30 Jan 2026 11:26:57 +0100 Subject: [PATCH 06/87] trying original kalman filter --- trackers/core/ocsort/kalman_filter_ocsort.py | 1577 ++++++++++++++++++ trackers/core/ocsort/tracker.py | 3 +- trackers/core/ocsort/tracklet.py | 150 +- 3 files changed, 1659 insertions(+), 71 deletions(-) create mode 100644 trackers/core/ocsort/kalman_filter_ocsort.py diff --git a/trackers/core/ocsort/kalman_filter_ocsort.py b/trackers/core/ocsort/kalman_filter_ocsort.py new file mode 100644 index 00000000..31c54177 --- /dev/null +++ b/trackers/core/ocsort/kalman_filter_ocsort.py @@ -0,0 +1,1577 @@ +""" +This module implements the linear Kalman filter in both an object +oriented and procedural form. The KalmanFilter class implements +the filter by storing the various matrices in instance variables, +minimizing the amount of bookkeeping you have to do. +All Kalman filters operate with a predict->update cycle. The +predict step, implemented with the method or function predict(), +uses the state transition matrix F to predict the state in the next +time period (epoch). The state is stored as a gaussian (x, P), where +x is the state (column) vector, and P is its covariance. Covariance +matrix Q specifies the process covariance. In Bayesian terms, this +prediction is called the *prior*, which you can think of colloquially +as the estimate prior to incorporating the measurement. +The update step, implemented with the method or function `update()`, +incorporates the measurement z with covariance R, into the state +estimate (x, P). The class stores the system uncertainty in S, +the innovation (residual between prediction and measurement in +measurement space) in y, and the Kalman gain in k. The procedural +form returns these variables to you. In Bayesian terms this computes +the *posterior* - the estimate after the information from the +measurement is incorporated. +Whether you use the OO form or procedural form is up to you. If +matrices such as H, R, and F are changing each epoch, you'll probably +opt to use the procedural form. If they are unchanging, the OO +form is perhaps easier to use since you won't need to keep track +of these matrices. This is especially useful if you are implementing +banks of filters or comparing various KF designs for performance; +a trivial coding bug could lead to using the wrong sets of matrices. +This module also offers an implementation of the RTS smoother, and +other helper functions, such as log likelihood computations. +The Saver class allows you to easily save the state of the +KalmanFilter class after every update +This module expects NumPy arrays for all values that expect +arrays, although in a few cases, particularly method parameters, +it will accept types that convert to NumPy arrays, such as lists +of lists. These exceptions are documented in the method or function. +Examples +-------- +The following example constructs a constant velocity kinematic +filter, filters noisy data, and plots the results. It also demonstrates +using the Saver class to save the state of the filter at each epoch. +.. code-block:: Python + import matplotlib.pyplot as plt + import numpy as np + from filterpy.kalman import KalmanFilter + from filterpy.common import Q_discrete_white_noise, Saver + r_std, q_std = 2., 0.003 + cv = KalmanFilter(dim_x=2, dim_z=1) + cv.x = np.array([[0., 1.]]) # position, velocity + cv.F = np.array([[1, dt],[ [0, 1]]) + cv.R = np.array([[r_std^^2]]) + f.H = np.array([[1., 0.]]) + f.P = np.diag([.1^^2, .03^^2) + f.Q = Q_discrete_white_noise(2, dt, q_std**2) + saver = Saver(cv) + for z in range(100): + cv.predict() + cv.update([z + randn() * r_std]) + saver.save() # save the filter's state + saver.to_array() + plt.plot(saver.x[:, 0]) + # plot all of the priors + plt.plot(saver.x_prior[:, 0]) + # plot mahalanobis distance + plt.figure() + plt.plot(saver.mahalanobis) +This code implements the same filter using the procedural form + x = np.array([[0., 1.]]) # position, velocity + F = np.array([[1, dt],[ [0, 1]]) + R = np.array([[r_std^^2]]) + H = np.array([[1., 0.]]) + P = np.diag([.1^^2, .03^^2) + Q = Q_discrete_white_noise(2, dt, q_std**2) + for z in range(100): + x, P = predict(x, P, F=F, Q=Q) + x, P = update(x, P, z=[z + randn() * r_std], R=R, H=H) + xs.append(x[0, 0]) + plt.plot(xs) +For more examples see the test subdirectory, or refer to the +book cited below. In it I both teach Kalman filtering from basic +principles, and teach the use of this library in great detail. +FilterPy library. +http://github.com/rlabbe/filterpy +Documentation at: +https://filterpy.readthedocs.org +Supporting book at: +https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python +This is licensed under an MIT license. See the readme.MD file +for more information. +Copyright 2014-2018 Roger R Labbe Jr. +""" + +from __future__ import absolute_import, division + +from copy import deepcopy +from math import log, exp, sqrt +import sys +import numpy as np +from numpy import dot, zeros, eye, isscalar, shape +import numpy.linalg as linalg +from filterpy.stats import logpdf +from filterpy.common import pretty_str, reshape_z + + +class KalmanFilterNew(object): + """ Implements a Kalman filter. You are responsible for setting the + various state variables to reasonable values; the defaults will + not give you a functional filter. + For now the best documentation is my free book Kalman and Bayesian + Filters in Python [2]_. The test files in this directory also give you a + basic idea of use, albeit without much description. + In brief, you will first construct this object, specifying the size of + the state vector with dim_x and the size of the measurement vector that + you will be using with dim_z. These are mostly used to perform size checks + when you assign values to the various matrices. For example, if you + specified dim_z=2 and then try to assign a 3x3 matrix to R (the + measurement noise matrix you will get an assert exception because R + should be 2x2. (If for whatever reason you need to alter the size of + things midstream just use the underscore version of the matrices to + assign directly: your_filter._R = a_3x3_matrix.) + After construction the filter will have default matrices created for you, + but you must specify the values for each. It’s usually easiest to just + overwrite them rather than assign to each element yourself. This will be + clearer in the example below. All are of type numpy.array. + Examples + -------- + Here is a filter that tracks position and velocity using a sensor that only + reads position. + First construct the object with the required dimensionality. Here the state + (`dim_x`) has 2 coefficients (position and velocity), and the measurement + (`dim_z`) has one. In FilterPy `x` is the state, `z` is the measurement. + .. code:: + from filterpy.kalman import KalmanFilter + f = KalmanFilter (dim_x=2, dim_z=1) + Assign the initial value for the state (position and velocity). You can do this + with a two dimensional array like so: + .. code:: + f.x = np.array([[2.], # position + [0.]]) # velocity + or just use a one dimensional array, which I prefer doing. + .. code:: + f.x = np.array([2., 0.]) + Define the state transition matrix: + .. code:: + f.F = np.array([[1.,1.], + [0.,1.]]) + Define the measurement function. Here we need to convert a position-velocity + vector into just a position vector, so we use: + .. code:: + f.H = np.array([[1., 0.]]) + Define the state's covariance matrix P. + .. code:: + f.P = np.array([[1000., 0.], + [ 0., 1000.] ]) + Now assign the measurement noise. Here the dimension is 1x1, so I can + use a scalar + .. code:: + f.R = 5 + I could have done this instead: + .. code:: + f.R = np.array([[5.]]) + Note that this must be a 2 dimensional array. + Finally, I will assign the process noise. Here I will take advantage of + another FilterPy library function: + .. code:: + from filterpy.common import Q_discrete_white_noise + f.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13) + Now just perform the standard predict/update loop: + .. code:: + while some_condition_is_true: + z = get_sensor_reading() + f.predict() + f.update(z) + do_something_with_estimate (f.x) + **Procedural Form** + This module also contains stand alone functions to perform Kalman filtering. + Use these if you are not a fan of objects. + **Example** + .. code:: + while True: + z, R = read_sensor() + x, P = predict(x, P, F, Q) + x, P = update(x, P, z, R, H) + See my book Kalman and Bayesian Filters in Python [2]_. + You will have to set the following attributes after constructing this + object for the filter to perform properly. Please note that there are + various checks in place to ensure that you have made everything the + 'correct' size. However, it is possible to provide incorrectly sized + arrays such that the linear algebra can not perform an operation. + It can also fail silently - you can end up with matrices of a size that + allows the linear algebra to work, but are the wrong shape for the problem + you are trying to solve. + Parameters + ---------- + dim_x : int + Number of state variables for the Kalman filter. For example, if + you are tracking the position and velocity of an object in two + dimensions, dim_x would be 4. + This is used to set the default size of P, Q, and u + dim_z : int + Number of of measurement inputs. For example, if the sensor + provides you with position in (x,y), dim_z would be 2. + dim_u : int (optional) + size of the control input, if it is being used. + Default value of 0 indicates it is not used. + compute_log_likelihood : bool (default = True) + Computes log likelihood by default, but this can be a slow + computation, so if you never use it you can turn this computation + off. + Attributes + ---------- + x : numpy.array(dim_x, 1) + Current state estimate. Any call to update() or predict() updates + this variable. + P : numpy.array(dim_x, dim_x) + Current state covariance matrix. Any call to update() or predict() + updates this variable. + x_prior : numpy.array(dim_x, 1) + Prior (predicted) state estimate. The *_prior and *_post attributes + are for convenience; they store the prior and posterior of the + current epoch. Read Only. + P_prior : numpy.array(dim_x, dim_x) + Prior (predicted) state covariance matrix. Read Only. + x_post : numpy.array(dim_x, 1) + Posterior (updated) state estimate. Read Only. + P_post : numpy.array(dim_x, dim_x) + Posterior (updated) state covariance matrix. Read Only. + z : numpy.array + Last measurement used in update(). Read only. + R : numpy.array(dim_z, dim_z) + Measurement noise covariance matrix. Also known as the + observation covariance. + Q : numpy.array(dim_x, dim_x) + Process noise covariance matrix. Also known as the transition + covariance. + F : numpy.array() + State Transition matrix. Also known as `A` in some formulation. + H : numpy.array(dim_z, dim_x) + Measurement function. Also known as the observation matrix, or as `C`. + y : numpy.array + Residual of the update step. Read only. + K : numpy.array(dim_x, dim_z) + Kalman gain of the update step. Read only. + S : numpy.array + System uncertainty (P projected to measurement space). Read only. + SI : numpy.array + Inverse system uncertainty. Read only. + log_likelihood : float + log-likelihood of the last measurement. Read only. + likelihood : float + likelihood of last measurement. Read only. + Computed from the log-likelihood. The log-likelihood can be very + small, meaning a large negative value such as -28000. Taking the + exp() of that results in 0.0, which can break typical algorithms + which multiply by this value, so by default we always return a + number >= sys.float_info.min. + mahalanobis : float + mahalanobis distance of the innovation. Read only. + inv : function, default numpy.linalg.inv + If you prefer another inverse function, such as the Moore-Penrose + pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv + This is only used to invert self.S. If you know it is diagonal, you + might choose to set it to filterpy.common.inv_diagonal, which is + several times faster than numpy.linalg.inv for diagonal matrices. + alpha : float + Fading memory setting. 1.0 gives the normal Kalman filter, and + values slightly larger than 1.0 (such as 1.02) give a fading + memory effect - previous measurements have less influence on the + filter's estimates. This formulation of the Fading memory filter + (there are many) is due to Dan Simon [1]_. + References + ---------- + .. [1] Dan Simon. "Optimal State Estimation." John Wiley & Sons. + p. 208-212. (2006) + .. [2] Roger Labbe. "Kalman and Bayesian Filters in Python" + https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python + """ + + def __init__(self, dim_x, dim_z, dim_u=0): + if dim_x < 1: + raise ValueError('dim_x must be 1 or greater') + if dim_z < 1: + raise ValueError('dim_z must be 1 or greater') + if dim_u < 0: + raise ValueError('dim_u must be 0 or greater') + + self.dim_x = dim_x + self.dim_z = dim_z + self.dim_u = dim_u + + self.x = zeros((dim_x, 1)) # state + self.P = eye(dim_x) # uncertainty covariance + self.Q = eye(dim_x) # process uncertainty + self.B = None # control transition matrix + self.F = eye(dim_x) # state transition matrix + self.H = zeros((dim_z, dim_x)) # measurement function + self.R = eye(dim_z) # measurement uncertainty + self._alpha_sq = 1. # fading memory control + self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation + self.z = np.array([[None]*self.dim_z]).T + + # gain and residual are computed during the innovation step. We + # save them so that in case you want to inspect them for various + # purposes + self.K = np.zeros((dim_x, dim_z)) # kalman gain + self.y = zeros((dim_z, 1)) + self.S = np.zeros((dim_z, dim_z)) # system uncertainty + self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty + + # identity matrix. Do not alter this. + self._I = np.eye(dim_x) + + # these will always be a copy of x,P after predict() is called + self.x_prior = self.x.copy() + self.P_prior = self.P.copy() + + # these will always be a copy of x,P after update() is called + self.x_post = self.x.copy() + self.P_post = self.P.copy() + + # Only computed only if requested via property + self._log_likelihood = log(sys.float_info.min) + self._likelihood = sys.float_info.min + self._mahalanobis = None + + # keep all observations + self.history_obs = [] + + self.inv = np.linalg.inv + + self.attr_saved = None + self.observed = False + + + def predict(self, u=None, B=None, F=None, Q=None): + """ + Predict next state (prior) using the Kalman filter state propagation + equations. + Parameters + ---------- + u : np.array, default 0 + Optional control vector. + B : np.array(dim_x, dim_u), or None + Optional control transition matrix; a value of None + will cause the filter to use `self.B`. + F : np.array(dim_x, dim_x), or None + Optional state transition matrix; a value of None + will cause the filter to use `self.F`. + Q : np.array(dim_x, dim_x), scalar, or None + Optional process noise matrix; a value of None will cause the + filter to use `self.Q`. + """ + + if B is None: + B = self.B + if F is None: + F = self.F + if Q is None: + Q = self.Q + elif isscalar(Q): + Q = eye(self.dim_x) * Q + + + # x = Fx + Bu + if B is not None and u is not None: + self.x = dot(F, self.x) + dot(B, u) + else: + self.x = dot(F, self.x) + + # P = FPF' + Q + self.P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q + + # save prior + self.x_prior = self.x.copy() + self.P_prior = self.P.copy() + + + + def freeze(self): + """ + Save the parameters before non-observation forward + """ + self.attr_saved = deepcopy(self.__dict__) + + + def unfreeze(self): + if self.attr_saved is not None: + new_history = deepcopy(self.history_obs) + self.__dict__ = self.attr_saved + # self.history_obs = new_history + self.history_obs = self.history_obs[:-1] + occur = [int(d is None) for d in new_history] + indices = np.where(np.array(occur)==0)[0] + index1 = indices[-2] + index2 = indices[-1] + box1 = new_history[index1] + x1, y1, s1, r1 = box1 + w1 = np.sqrt(s1 * r1) + h1 = np.sqrt(s1 / r1) + box2 = new_history[index2] + x2, y2, s2, r2 = box2 + w2 = np.sqrt(s2 * r2) + h2 = np.sqrt(s2 / r2) + time_gap = index2 - index1 + dx = (x2-x1)/time_gap + dy = (y2-y1)/time_gap + dw = (w2-w1)/time_gap + dh = (h2-h1)/time_gap + for i in range(index2 - index1): + """ + The default virtual trajectory generation is by linear + motion (constant speed hypothesis), you could modify this + part to implement your own. + """ + x = x1 + (i+1) * dx + y = y1 + (i+1) * dy + w = w1 + (i+1) * dw + h = h1 + (i+1) * dh + s = w * h + r = w / float(h) + new_box = np.array([x, y, s, r]).reshape((4, 1)) + """ + I still use predict-update loop here to refresh the parameters, + but this can be faster by directly modifying the internal parameters + as suggested in the paper. I keep this naive but slow way for + easy read and understanding + """ + self.update(new_box) + if not i == (index2-index1-1): + self.predict() + + + def update(self, z, R=None, H=None): + """ + Add a new measurement (z) to the Kalman filter. + If z is None, nothing is computed. However, x_post and P_post are + updated with the prior (x_prior, P_prior), and self.z is set to None. + Parameters + ---------- + z : (dim_z, 1): array_like + measurement for this update. z can be a scalar if dim_z is 1, + otherwise it must be convertible to a column vector. + If you pass in a value of H, z must be a column vector the + of the correct size. + R : np.array, scalar, or None + Optionally provide R to override the measurement noise for this + one call, otherwise self.R will be used. + H : np.array, or None + Optionally provide H to override the measurement function for this + one call, otherwise self.H will be used. + """ + + # set to None to force recompute + self._log_likelihood = None + self._likelihood = None + self._mahalanobis = None + + # append the observation + self.history_obs.append(z) + + if z is None: + if self.observed: + """ + Got no observation so freeze the current parameters for future + potential online smoothing. + """ + self.freeze() + self.observed = False + self.z = np.array([[None]*self.dim_z]).T + self.x_post = self.x.copy() + self.P_post = self.P.copy() + self.y = zeros((self.dim_z, 1)) + return + + # self.observed = True + if not self.observed: + """ + Get observation, use online smoothing to re-update parameters + """ + self.unfreeze() + self.observed = True + + if R is None: + R = self.R + elif isscalar(R): + R = eye(self.dim_z) * R + + if H is None: + z = reshape_z(z, self.dim_z, self.x.ndim) + H = self.H + + # y = z - Hx + # error (residual) between measurement and prediction + self.y = z - dot(H, self.x) + + # common subexpression for speed + PHT = dot(self.P, H.T) + + # S = HPH' + R + # project system uncertainty into measurement space + self.S = dot(H, PHT) + R + self.SI = self.inv(self.S) + # K = PH'inv(S) + # map system uncertainty into kalman gain + self.K = dot(PHT, self.SI) + + # x = x + Ky + # predict new x with residual scaled by the kalman gain + self.x = self.x + dot(self.K, self.y) + + # P = (I-KH)P(I-KH)' + KRK' + # This is more numerically stable + # and works for non-optimal K vs the equation + # P = (I-KH)P usually seen in the literature. + + I_KH = self._I - dot(self.K, H) + self.P = dot(dot(I_KH, self.P), I_KH.T) + dot(dot(self.K, R), self.K.T) + + # save measurement and posterior state + self.z = deepcopy(z) + self.x_post = self.x.copy() + self.P_post = self.P.copy() + + def predict_steadystate(self, u=0, B=None): + """ + Predict state (prior) using the Kalman filter state propagation + equations. Only x is updated, P is left unchanged. See + update_steadstate() for a longer explanation of when to use this + method. + Parameters + ---------- + u : np.array + Optional control vector. If non-zero, it is multiplied by B + to create the control input into the system. + B : np.array(dim_x, dim_u), or None + Optional control transition matrix; a value of None + will cause the filter to use `self.B`. + """ + + if B is None: + B = self.B + + # x = Fx + Bu + if B is not None: + self.x = dot(self.F, self.x) + dot(B, u) + else: + self.x = dot(self.F, self.x) + + # save prior + self.x_prior = self.x.copy() + self.P_prior = self.P.copy() + + def update_steadystate(self, z): + """ + Add a new measurement (z) to the Kalman filter without recomputing + the Kalman gain K, the state covariance P, or the system + uncertainty S. + You can use this for LTI systems since the Kalman gain and covariance + converge to a fixed value. Precompute these and assign them explicitly, + or run the Kalman filter using the normal predict()/update(0 cycle + until they converge. + The main advantage of this call is speed. We do significantly less + computation, notably avoiding a costly matrix inversion. + Use in conjunction with predict_steadystate(), otherwise P will grow + without bound. + Parameters + ---------- + z : (dim_z, 1): array_like + measurement for this update. z can be a scalar if dim_z is 1, + otherwise it must be convertible to a column vector. + Examples + -------- + >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter + >>> # let filter converge on representative data, then save k and P + >>> for i in range(100): + >>> cv.predict() + >>> cv.update([i, i, i]) + >>> saved_k = np.copy(cv.K) + >>> saved_P = np.copy(cv.P) + later on: + >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter + >>> cv.K = np.copy(saved_K) + >>> cv.P = np.copy(saved_P) + >>> for i in range(100): + >>> cv.predict_steadystate() + >>> cv.update_steadystate([i, i, i]) + """ + + # set to None to force recompute + self._log_likelihood = None + self._likelihood = None + self._mahalanobis = None + + if z is None: + self.z = np.array([[None]*self.dim_z]).T + self.x_post = self.x.copy() + self.P_post = self.P.copy() + self.y = zeros((self.dim_z, 1)) + return + + z = reshape_z(z, self.dim_z, self.x.ndim) + + # y = z - Hx + # error (residual) between measurement and prediction + self.y = z - dot(self.H, self.x) + + # x = x + Ky + # predict new x with residual scaled by the kalman gain + self.x = self.x + dot(self.K, self.y) + + self.z = deepcopy(z) + self.x_post = self.x.copy() + self.P_post = self.P.copy() + + # set to None to force recompute + self._log_likelihood = None + self._likelihood = None + self._mahalanobis = None + + def update_correlated(self, z, R=None, H=None): + """ Add a new measurement (z) to the Kalman filter assuming that + process noise and measurement noise are correlated as defined in + the `self.M` matrix. + A partial derivation can be found in [1] + If z is None, nothing is changed. + Parameters + ---------- + z : (dim_z, 1): array_like + measurement for this update. z can be a scalar if dim_z is 1, + otherwise it must be convertible to a column vector. + R : np.array, scalar, or None + Optionally provide R to override the measurement noise for this + one call, otherwise self.R will be used. + H : np.array, or None + Optionally provide H to override the measurement function for this + one call, otherwise self.H will be used. + References + ---------- + .. [1] Bulut, Y. (2011). Applied Kalman filter theory (Doctoral dissertation, Northeastern University). + http://people.duke.edu/~hpgavin/SystemID/References/Balut-KalmanFilter-PhD-NEU-2011.pdf + """ + + # set to None to force recompute + self._log_likelihood = None + self._likelihood = None + self._mahalanobis = None + + if z is None: + self.z = np.array([[None]*self.dim_z]).T + self.x_post = self.x.copy() + self.P_post = self.P.copy() + self.y = zeros((self.dim_z, 1)) + return + + if R is None: + R = self.R + elif isscalar(R): + R = eye(self.dim_z) * R + + # rename for readability and a tiny extra bit of speed + if H is None: + z = reshape_z(z, self.dim_z, self.x.ndim) + H = self.H + + # handle special case: if z is in form [[z]] but x is not a column + # vector dimensions will not match + if self.x.ndim == 1 and shape(z) == (1, 1): + z = z[0] + + if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3) + z = np.asarray([z]) + + # y = z - Hx + # error (residual) between measurement and prediction + self.y = z - dot(H, self.x) + + # common subexpression for speed + PHT = dot(self.P, H.T) + + # project system uncertainty into measurement space + self.S = dot(H, PHT) + dot(H, self.M) + dot(self.M.T, H.T) + R + self.SI = self.inv(self.S) + + # K = PH'inv(S) + # map system uncertainty into kalman gain + self.K = dot(PHT + self.M, self.SI) + + # x = x + Ky + # predict new x with residual scaled by the kalman gain + self.x = self.x + dot(self.K, self.y) + self.P = self.P - dot(self.K, dot(H, self.P) + self.M.T) + + self.z = deepcopy(z) + self.x_post = self.x.copy() + self.P_post = self.P.copy() + + def batch_filter(self, zs, Fs=None, Qs=None, Hs=None, + Rs=None, Bs=None, us=None, update_first=False, + saver=None): + """ Batch processes a sequences of measurements. + Parameters + ---------- + zs : list-like + list of measurements at each time step `self.dt`. Missing + measurements must be represented by `None`. + Fs : None, list-like, default=None + optional value or list of values to use for the state transition + matrix F. + If Fs is None then self.F is used for all epochs. + Otherwise it must contain a list-like list of F's, one for + each epoch. This allows you to have varying F per epoch. + Qs : None, np.array or list-like, default=None + optional value or list of values to use for the process error + covariance Q. + If Qs is None then self.Q is used for all epochs. + Otherwise it must contain a list-like list of Q's, one for + each epoch. This allows you to have varying Q per epoch. + Hs : None, np.array or list-like, default=None + optional list of values to use for the measurement matrix H. + If Hs is None then self.H is used for all epochs. + If Hs contains a single matrix, then it is used as H for all + epochs. + Otherwise it must contain a list-like list of H's, one for + each epoch. This allows you to have varying H per epoch. + Rs : None, np.array or list-like, default=None + optional list of values to use for the measurement error + covariance R. + If Rs is None then self.R is used for all epochs. + Otherwise it must contain a list-like list of R's, one for + each epoch. This allows you to have varying R per epoch. + Bs : None, np.array or list-like, default=None + optional list of values to use for the control transition matrix B. + If Bs is None then self.B is used for all epochs. + Otherwise it must contain a list-like list of B's, one for + each epoch. This allows you to have varying B per epoch. + us : None, np.array or list-like, default=None + optional list of values to use for the control input vector; + If us is None then None is used for all epochs (equivalent to 0, + or no control input). + Otherwise it must contain a list-like list of u's, one for + each epoch. + update_first : bool, optional, default=False + controls whether the order of operations is update followed by + predict, or predict followed by update. Default is predict->update. + saver : filterpy.common.Saver, optional + filterpy.common.Saver object. If provided, saver.save() will be + called after every epoch + Returns + ------- + means : np.array((n,dim_x,1)) + array of the state for each time step after the update. Each entry + is an np.array. In other words `means[k,:]` is the state at step + `k`. + covariance : np.array((n,dim_x,dim_x)) + array of the covariances for each time step after the update. + In other words `covariance[k,:,:]` is the covariance at step `k`. + means_predictions : np.array((n,dim_x,1)) + array of the state for each time step after the predictions. Each + entry is an np.array. In other words `means[k,:]` is the state at + step `k`. + covariance_predictions : np.array((n,dim_x,dim_x)) + array of the covariances for each time step after the prediction. + In other words `covariance[k,:,:]` is the covariance at step `k`. + Examples + -------- + .. code-block:: Python + # this example demonstrates tracking a measurement where the time + # between measurement varies, as stored in dts. This requires + # that F be recomputed for each epoch. The output is then smoothed + # with an RTS smoother. + zs = [t + random.randn()*4 for t in range (40)] + Fs = [np.array([[1., dt], [0, 1]] for dt in dts] + (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs) + (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs) + """ + + #pylint: disable=too-many-statements + n = np.size(zs, 0) + if Fs is None: + Fs = [self.F] * n + if Qs is None: + Qs = [self.Q] * n + if Hs is None: + Hs = [self.H] * n + if Rs is None: + Rs = [self.R] * n + if Bs is None: + Bs = [self.B] * n + if us is None: + us = [0] * n + + # mean estimates from Kalman Filter + if self.x.ndim == 1: + means = zeros((n, self.dim_x)) + means_p = zeros((n, self.dim_x)) + else: + means = zeros((n, self.dim_x, 1)) + means_p = zeros((n, self.dim_x, 1)) + + # state covariances from Kalman Filter + covariances = zeros((n, self.dim_x, self.dim_x)) + covariances_p = zeros((n, self.dim_x, self.dim_x)) + + if update_first: + for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): + + self.update(z, R=R, H=H) + means[i, :] = self.x + covariances[i, :, :] = self.P + + self.predict(u=u, B=B, F=F, Q=Q) + means_p[i, :] = self.x + covariances_p[i, :, :] = self.P + + if saver is not None: + saver.save() + else: + for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): + + self.predict(u=u, B=B, F=F, Q=Q) + means_p[i, :] = self.x + covariances_p[i, :, :] = self.P + + self.update(z, R=R, H=H) + means[i, :] = self.x + covariances[i, :, :] = self.P + + if saver is not None: + saver.save() + + return (means, covariances, means_p, covariances_p) + + def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv): + """ + Runs the Rauch-Tung-Striebel Kalman smoother on a set of + means and covariances computed by a Kalman filter. The usual input + would come from the output of `KalmanFilter.batch_filter()`. + Parameters + ---------- + Xs : numpy.array + array of the means (state variable x) of the output of a Kalman + filter. + Ps : numpy.array + array of the covariances of the output of a kalman filter. + Fs : list-like collection of numpy.array, optional + State transition matrix of the Kalman filter at each time step. + Optional, if not provided the filter's self.F will be used + Qs : list-like collection of numpy.array, optional + Process noise of the Kalman filter at each time step. Optional, + if not provided the filter's self.Q will be used + inv : function, default numpy.linalg.inv + If you prefer another inverse function, such as the Moore-Penrose + pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv + Returns + ------- + x : numpy.ndarray + smoothed means + P : numpy.ndarray + smoothed state covariances + K : numpy.ndarray + smoother gain at each step + Pp : numpy.ndarray + Predicted state covariances + Examples + -------- + .. code-block:: Python + zs = [t + random.randn()*4 for t in range (40)] + (mu, cov, _, _) = kalman.batch_filter(zs) + (x, P, K, Pp) = rts_smoother(mu, cov, kf.F, kf.Q) + """ + + if len(Xs) != len(Ps): + raise ValueError('length of Xs and Ps must be the same') + + n = Xs.shape[0] + dim_x = Xs.shape[1] + + if Fs is None: + Fs = [self.F] * n + if Qs is None: + Qs = [self.Q] * n + + # smoother gain + K = zeros((n, dim_x, dim_x)) + + x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy() + for k in range(n-2, -1, -1): + Pp[k] = dot(dot(Fs[k+1], P[k]), Fs[k+1].T) + Qs[k+1] + + #pylint: disable=bad-whitespace + K[k] = dot(dot(P[k], Fs[k+1].T), inv(Pp[k])) + x[k] += dot(K[k], x[k+1] - dot(Fs[k+1], x[k])) + P[k] += dot(dot(K[k], P[k+1] - Pp[k]), K[k].T) + + return (x, P, K, Pp) + + def get_prediction(self, u=None, B=None, F=None, Q=None): + """ + Predict next state (prior) using the Kalman filter state propagation + equations and returns it without modifying the object. + Parameters + ---------- + u : np.array, default 0 + Optional control vector. + B : np.array(dim_x, dim_u), or None + Optional control transition matrix; a value of None + will cause the filter to use `self.B`. + F : np.array(dim_x, dim_x), or None + Optional state transition matrix; a value of None + will cause the filter to use `self.F`. + Q : np.array(dim_x, dim_x), scalar, or None + Optional process noise matrix; a value of None will cause the + filter to use `self.Q`. + Returns + ------- + (x, P) : tuple + State vector and covariance array of the prediction. + """ + + if B is None: + B = self.B + if F is None: + F = self.F + if Q is None: + Q = self.Q + elif isscalar(Q): + Q = eye(self.dim_x) * Q + + # x = Fx + Bu + if B is not None and u is not None: + x = dot(F, self.x) + dot(B, u) + else: + x = dot(F, self.x) + + # P = FPF' + Q + P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q + + return x, P + + def get_update(self, z=None): + """ + Computes the new estimate based on measurement `z` and returns it + without altering the state of the filter. + Parameters + ---------- + z : (dim_z, 1): array_like + measurement for this update. z can be a scalar if dim_z is 1, + otherwise it must be convertible to a column vector. + Returns + ------- + (x, P) : tuple + State vector and covariance array of the update. + """ + + if z is None: + return self.x, self.P + z = reshape_z(z, self.dim_z, self.x.ndim) + + R = self.R + H = self.H + P = self.P + x = self.x + + # error (residual) between measurement and prediction + y = z - dot(H, x) + + # common subexpression for speed + PHT = dot(P, H.T) + + # project system uncertainty into measurement space + S = dot(H, PHT) + R + + # map system uncertainty into kalman gain + K = dot(PHT, self.inv(S)) + + # predict new x with residual scaled by the kalman gain + x = x + dot(K, y) + + # P = (I-KH)P(I-KH)' + KRK' + I_KH = self._I - dot(K, H) + P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T) + + return x, P + + def residual_of(self, z): + """ + Returns the residual for the given measurement (z). Does not alter + the state of the filter. + """ + z = reshape_z(z, self.dim_z, self.x.ndim) + return z - dot(self.H, self.x_prior) + + def measurement_of_state(self, x): + """ + Helper function that converts a state into a measurement. + Parameters + ---------- + x : np.array + kalman state vector + Returns + ------- + z : (dim_z, 1): array_like + measurement for this update. z can be a scalar if dim_z is 1, + otherwise it must be convertible to a column vector. + """ + + return dot(self.H, x) + + @property + def log_likelihood(self): + """ + log-likelihood of the last measurement. + """ + if self._log_likelihood is None: + self._log_likelihood = logpdf(x=self.y, cov=self.S) + return self._log_likelihood + + @property + def likelihood(self): + """ + Computed from the log-likelihood. The log-likelihood can be very + small, meaning a large negative value such as -28000. Taking the + exp() of that results in 0.0, which can break typical algorithms + which multiply by this value, so by default we always return a + number >= sys.float_info.min. + """ + if self._likelihood is None: + self._likelihood = exp(self.log_likelihood) + if self._likelihood == 0: + self._likelihood = sys.float_info.min + return self._likelihood + + @property + def mahalanobis(self): + """" + Mahalanobis distance of measurement. E.g. 3 means measurement + was 3 standard deviations away from the predicted value. + Returns + ------- + mahalanobis : float + """ + if self._mahalanobis is None: + self._mahalanobis = sqrt(float(dot(dot(self.y.T, self.SI), self.y))) + return self._mahalanobis + + @property + def alpha(self): + """ + Fading memory setting. 1.0 gives the normal Kalman filter, and + values slightly larger than 1.0 (such as 1.02) give a fading + memory effect - previous measurements have less influence on the + filter's estimates. This formulation of the Fading memory filter + (there are many) is due to Dan Simon [1]_. + """ + return self._alpha_sq**.5 + + def log_likelihood_of(self, z): + """ + log likelihood of the measurement `z`. This should only be called + after a call to update(). Calling after predict() will yield an + incorrect result.""" + + if z is None: + return log(sys.float_info.min) + return logpdf(z, dot(self.H, self.x), self.S) + + @alpha.setter + def alpha(self, value): + if not np.isscalar(value) or value < 1: + raise ValueError('alpha must be a float greater than 1') + + self._alpha_sq = value**2 + + def __repr__(self): + return '\n'.join([ + 'KalmanFilter object', + pretty_str('dim_x', self.dim_x), + pretty_str('dim_z', self.dim_z), + pretty_str('dim_u', self.dim_u), + pretty_str('x', self.x), + pretty_str('P', self.P), + pretty_str('x_prior', self.x_prior), + pretty_str('P_prior', self.P_prior), + pretty_str('x_post', self.x_post), + pretty_str('P_post', self.P_post), + pretty_str('F', self.F), + pretty_str('Q', self.Q), + pretty_str('R', self.R), + pretty_str('H', self.H), + pretty_str('K', self.K), + pretty_str('y', self.y), + pretty_str('S', self.S), + pretty_str('SI', self.SI), + pretty_str('M', self.M), + pretty_str('B', self.B), + pretty_str('z', self.z), + pretty_str('log-likelihood', self.log_likelihood), + pretty_str('likelihood', self.likelihood), + pretty_str('mahalanobis', self.mahalanobis), + pretty_str('alpha', self.alpha), + pretty_str('inv', self.inv) + ]) + + def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None): + """ + Performs a series of asserts to check that the size of everything + is what it should be. This can help you debug problems in your design. + If you pass in H, R, F, Q those will be used instead of this object's + value for those matrices. + Testing `z` (the measurement) is problamatic. x is a vector, and can be + implemented as either a 1D array or as a nx1 column vector. Thus Hx + can be of different shapes. Then, if Hx is a single value, it can + be either a 1D array or 2D vector. If either is true, z can reasonably + be a scalar (either '3' or np.array('3') are scalars under this + definition), a 1D, 1 element array, or a 2D, 1 element array. You are + allowed to pass in any combination that works. + """ + + if H is None: + H = self.H + if R is None: + R = self.R + if F is None: + F = self.F + if Q is None: + Q = self.Q + x = self.x + P = self.P + + assert x.ndim == 1 or x.ndim == 2, \ + "x must have one or two dimensions, but has {}".format(x.ndim) + + if x.ndim == 1: + assert x.shape[0] == self.dim_x, \ + "Shape of x must be ({},{}), but is {}".format( + self.dim_x, 1, x.shape) + else: + assert x.shape == (self.dim_x, 1), \ + "Shape of x must be ({},{}), but is {}".format( + self.dim_x, 1, x.shape) + + assert P.shape == (self.dim_x, self.dim_x), \ + "Shape of P must be ({},{}), but is {}".format( + self.dim_x, self.dim_x, P.shape) + + assert Q.shape == (self.dim_x, self.dim_x), \ + "Shape of Q must be ({},{}), but is {}".format( + self.dim_x, self.dim_x, P.shape) + + assert F.shape == (self.dim_x, self.dim_x), \ + "Shape of F must be ({},{}), but is {}".format( + self.dim_x, self.dim_x, F.shape) + + assert np.ndim(H) == 2, \ + "Shape of H must be (dim_z, {}), but is {}".format( + P.shape[0], shape(H)) + + assert H.shape[1] == P.shape[0], \ + "Shape of H must be (dim_z, {}), but is {}".format( + P.shape[0], H.shape) + + # shape of R must be the same as HPH' + hph_shape = (H.shape[0], H.shape[0]) + r_shape = shape(R) + + if H.shape[0] == 1: + # r can be scalar, 1D, or 2D in this case + assert r_shape in [(), (1,), (1, 1)], \ + "R must be scalar or one element array, but is shaped {}".format( + r_shape) + else: + assert r_shape == hph_shape, \ + "shape of R should be {} but it is {}".format(hph_shape, r_shape) + + + if z is not None: + z_shape = shape(z) + else: + z_shape = (self.dim_z, 1) + + # H@x must have shape of z + Hx = dot(H, x) + + if z_shape == (): # scalar or np.array(scalar) + assert Hx.ndim == 1 or shape(Hx) == (1, 1), \ + "shape of z should be {}, not {} for the given H".format( + shape(Hx), z_shape) + + elif shape(Hx) == (1,): + assert z_shape[0] == 1, 'Shape of z must be {} for the given H'.format(shape(Hx)) + + else: + assert (z_shape == shape(Hx) or + (len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1))), \ + "shape of z should be {}, not {} for the given H".format( + shape(Hx), z_shape) + + if np.ndim(Hx) > 1 and shape(Hx) != (1, 1): + assert shape(Hx) == z_shape, \ + 'shape of z should be {} for the given H, but it is {}'.format( + shape(Hx), z_shape) + + +def update(x, P, z, R, H=None, return_all=False): + """ + Add a new measurement (z) to the Kalman filter. If z is None, nothing + is changed. + This can handle either the multidimensional or unidimensional case. If + all parameters are floats instead of arrays the filter will still work, + and return floats for x, P as the result. + update(1, 2, 1, 1, 1) # univariate + update(x, P, 1 + Parameters + ---------- + x : numpy.array(dim_x, 1), or float + State estimate vector + P : numpy.array(dim_x, dim_x), or float + Covariance matrix + z : (dim_z, 1): array_like + measurement for this update. z can be a scalar if dim_z is 1, + otherwise it must be convertible to a column vector. + R : numpy.array(dim_z, dim_z), or float + Measurement noise matrix + H : numpy.array(dim_x, dim_x), or float, optional + Measurement function. If not provided, a value of 1 is assumed. + return_all : bool, default False + If true, y, K, S, and log_likelihood are returned, otherwise + only x and P are returned. + Returns + ------- + x : numpy.array + Posterior state estimate vector + P : numpy.array + Posterior covariance matrix + y : numpy.array or scalar + Residua. Difference between measurement and state in measurement space + K : numpy.array + Kalman gain + S : numpy.array + System uncertainty in measurement space + log_likelihood : float + log likelihood of the measurement + """ + + #pylint: disable=bare-except + + if z is None: + if return_all: + return x, P, None, None, None, None + return x, P + + if H is None: + H = np.array([1]) + + if np.isscalar(H): + H = np.array([H]) + + Hx = np.atleast_1d(dot(H, x)) + z = reshape_z(z, Hx.shape[0], x.ndim) + + # error (residual) between measurement and prediction + y = z - Hx + + # project system uncertainty into measurement space + S = dot(dot(H, P), H.T) + R + + + # map system uncertainty into kalman gain + try: + K = dot(dot(P, H.T), linalg.inv(S)) + except: + # can't invert a 1D array, annoyingly + K = dot(dot(P, H.T), 1./S) + + + # predict new x with residual scaled by the kalman gain + x = x + dot(K, y) + + # P = (I-KH)P(I-KH)' + KRK' + KH = dot(K, H) + + try: + I_KH = np.eye(KH.shape[0]) - KH + except: + I_KH = np.array([1 - KH]) + P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T) + + + if return_all: + # compute log likelihood + log_likelihood = logpdf(z, dot(H, x), S) + return x, P, y, K, S, log_likelihood + return x, P + + +def update_steadystate(x, z, K, H=None): + """ + Add a new measurement (z) to the Kalman filter. If z is None, nothing + is changed. + Parameters + ---------- + x : numpy.array(dim_x, 1), or float + State estimate vector + z : (dim_z, 1): array_like + measurement for this update. z can be a scalar if dim_z is 1, + otherwise it must be convertible to a column vector. + K : numpy.array, or float + Kalman gain matrix + H : numpy.array(dim_x, dim_x), or float, optional + Measurement function. If not provided, a value of 1 is assumed. + Returns + ------- + x : numpy.array + Posterior state estimate vector + Examples + -------- + This can handle either the multidimensional or unidimensional case. If + all parameters are floats instead of arrays the filter will still work, + and return floats for x, P as the result. + >>> update_steadystate(1, 2, 1) # univariate + >>> update_steadystate(x, P, z, H) + """ + + + if z is None: + return x + + if H is None: + H = np.array([1]) + + if np.isscalar(H): + H = np.array([H]) + + Hx = np.atleast_1d(dot(H, x)) + z = reshape_z(z, Hx.shape[0], x.ndim) + + # error (residual) between measurement and prediction + y = z - Hx + + # estimate new x with residual scaled by the kalman gain + return x + dot(K, y) + + +def predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.): + """ + Predict next state (prior) using the Kalman filter state propagation + equations. + Parameters + ---------- + x : numpy.array + State estimate vector + P : numpy.array + Covariance matrix + F : numpy.array() + State Transition matrix + Q : numpy.array, Optional + Process noise matrix + u : numpy.array, Optional, default 0. + Control vector. If non-zero, it is multiplied by B + to create the control input into the system. + B : numpy.array, optional, default 0. + Control transition matrix. + alpha : float, Optional, default=1.0 + Fading memory setting. 1.0 gives the normal Kalman filter, and + values slightly larger than 1.0 (such as 1.02) give a fading + memory effect - previous measurements have less influence on the + filter's estimates. This formulation of the Fading memory filter + (there are many) is due to Dan Simon + Returns + ------- + x : numpy.array + Prior state estimate vector + P : numpy.array + Prior covariance matrix + """ + + if np.isscalar(F): + F = np.array(F) + x = dot(F, x) + dot(B, u) + P = (alpha * alpha) * dot(dot(F, P), F.T) + Q + + return x, P + + +def predict_steadystate(x, F=1, u=0, B=1): + """ + Predict next state (prior) using the Kalman filter state propagation + equations. This steady state form only computes x, assuming that the + covariance is constant. + Parameters + ---------- + x : numpy.array + State estimate vector + P : numpy.array + Covariance matrix + F : numpy.array() + State Transition matrix + u : numpy.array, Optional, default 0. + Control vector. If non-zero, it is multiplied by B + to create the control input into the system. + B : numpy.array, optional, default 0. + Control transition matrix. + Returns + ------- + x : numpy.array + Prior state estimate vector + """ + + if np.isscalar(F): + F = np.array(F) + x = dot(F, x) + dot(B, u) + + return x + + + +def batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None, + update_first=False, saver=None): + """ + Batch processes a sequences of measurements. + Parameters + ---------- + zs : list-like + list of measurements at each time step. Missing measurements must be + represented by None. + Fs : list-like + list of values to use for the state transition matrix matrix. + Qs : list-like + list of values to use for the process error + covariance. + Hs : list-like + list of values to use for the measurement matrix. + Rs : list-like + list of values to use for the measurement error + covariance. + Bs : list-like, optional + list of values to use for the control transition matrix; + a value of None in any position will cause the filter + to use `self.B` for that time step. + us : list-like, optional + list of values to use for the control input vector; + a value of None in any position will cause the filter to use + 0 for that time step. + update_first : bool, optional + controls whether the order of operations is update followed by + predict, or predict followed by update. Default is predict->update. + saver : filterpy.common.Saver, optional + filterpy.common.Saver object. If provided, saver.save() will be + called after every epoch + Returns + ------- + means : np.array((n,dim_x,1)) + array of the state for each time step after the update. Each entry + is an np.array. In other words `means[k,:]` is the state at step + `k`. + covariance : np.array((n,dim_x,dim_x)) + array of the covariances for each time step after the update. + In other words `covariance[k,:,:]` is the covariance at step `k`. + means_predictions : np.array((n,dim_x,1)) + array of the state for each time step after the predictions. Each + entry is an np.array. In other words `means[k,:]` is the state at + step `k`. + covariance_predictions : np.array((n,dim_x,dim_x)) + array of the covariances for each time step after the prediction. + In other words `covariance[k,:,:]` is the covariance at step `k`. + Examples + -------- + .. code-block:: Python + zs = [t + random.randn()*4 for t in range (40)] + Fs = [kf.F for t in range (40)] + Hs = [kf.H for t in range (40)] + (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None, + Bs=None, us=None, update_first=False) + (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None) + """ + + n = np.size(zs, 0) + dim_x = x.shape[0] + + # mean estimates from Kalman Filter + if x.ndim == 1: + means = zeros((n, dim_x)) + means_p = zeros((n, dim_x)) + else: + means = zeros((n, dim_x, 1)) + means_p = zeros((n, dim_x, 1)) + + # state covariances from Kalman Filter + covariances = zeros((n, dim_x, dim_x)) + covariances_p = zeros((n, dim_x, dim_x)) + + if us is None: + us = [0.] * n + Bs = [0.] * n + + if update_first: + for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): + + x, P = update(x, P, z, R=R, H=H) + means[i, :] = x + covariances[i, :, :] = P + + x, P = predict(x, P, u=u, B=B, F=F, Q=Q) + means_p[i, :] = x + covariances_p[i, :, :] = P + if saver is not None: + saver.save() + else: + for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): + + x, P = predict(x, P, u=u, B=B, F=F, Q=Q) + means_p[i, :] = x + covariances_p[i, :, :] = P + + x, P = update(x, P, z, R=R, H=H) + means[i, :] = x + covariances[i, :, :] = P + if saver is not None: + saver.save() + + return (means, covariances, means_p, covariances_p) + + + +def rts_smoother(Xs, Ps, Fs, Qs): + """ + Runs the Rauch-Tung-Striebel Kalman smoother on a set of + means and covariances computed by a Kalman filter. The usual input + would come from the output of `KalmanFilter.batch_filter()`. + Parameters + ---------- + Xs : numpy.array + array of the means (state variable x) of the output of a Kalman + filter. + Ps : numpy.array + array of the covariances of the output of a kalman filter. + Fs : list-like collection of numpy.array + State transition matrix of the Kalman filter at each time step. + Qs : list-like collection of numpy.array, optional + Process noise of the Kalman filter at each time step. + Returns + ------- + x : numpy.ndarray + smoothed means + P : numpy.ndarray + smoothed state covariances + K : numpy.ndarray + smoother gain at each step + pP : numpy.ndarray + predicted state covariances + Examples + -------- + .. code-block:: Python + zs = [t + random.randn()*4 for t in range (40)] + (mu, cov, _, _) = kalman.batch_filter(zs) + (x, P, K, pP) = rts_smoother(mu, cov, kf.F, kf.Q) + """ + + if len(Xs) != len(Ps): + raise ValueError('length of Xs and Ps must be the same') + + n = Xs.shape[0] + dim_x = Xs.shape[1] + + # smoother gain + K = zeros((n, dim_x, dim_x)) + x, P, pP = Xs.copy(), Ps.copy(), Ps.copy() + + for k in range(n-2, -1, -1): + pP[k] = dot(dot(Fs[k], P[k]), Fs[k].T) + Qs[k] + + #pylint: disable=bad-whitespace + K[k] = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k])) + x[k] += dot(K[k], x[k+1] - dot(Fs[k], x[k])) + P[k] += dot(dot(K[k], P[k+1] - pP[k]), K[k].T) + + return (x, P, K, pP) \ No newline at end of file diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 35b85047..d820d8de 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -216,7 +216,8 @@ def update(self, detections: sv.Detections) -> sv.Detections: [-1] * len(left_detections), dtype=int ) updated_detections.append(left_detections) - + for m in _ocr_unmatched_tracks: + self.tracks[unmatched_tracks[m]].update(None) else: self.tracks = self.activate_or_kill_tracklets() self._spawn_new_tracklets(detections, unmatched_detections) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 8f61200e..716e7bb8 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -1,11 +1,12 @@ import numpy as np -from supervision import xyxy_to_xywh +from trackers.core.ocsort.kalman_filter_ocsort import ( + KalmanFilterNew as KalmanFilterOCSORT, +) from trackers.utils.converters import ( xcycsr_to_xyxy, xyxy_to_xcycsr, ) -from trackers.utils.kalman_filter import KalmanFilter class OCSORTTracklet: @@ -32,21 +33,30 @@ class OCSORTTracklet: def __init__(self, initial_bbox) -> None: self.age = 0 # state format: (x, y, s, r, vx, vy, vs). As detailed in SORT paper, r is the aspect ratio and constant! # noqa: E501 - self.kalman_filter = KalmanFilter( - bbox=xyxy_to_xcycsr(initial_bbox), - state_dim=7, - state_transition_matrix=np.array( - [ - [1, 0, 0, 0, 1, 0, 0], - [0, 1, 0, 0, 0, 1, 0], - [0, 0, 1, 0, 0, 0, 1], - [0, 0, 0, 1, 0, 0, 0], - [0, 0, 0, 0, 1, 0, 0], - [0, 0, 0, 0, 0, 1, 0], - [0, 0, 0, 0, 0, 0, 1], - ] - ), - ) + # self.kalman_filter = KalmanFilter( + # bbox=xyxy_to_xcycsr(initial_bbox), + # state_dim=7, + # state_transition_matrix=np.array( + # [ + # [1, 0, 0, 0, 1, 0, 0], + # [0, 1, 0, 0, 0, 1, 0], + # [0, 0, 1, 0, 0, 0, 1], + # [0, 0, 0, 1, 0, 0, 0], + # [0, 0, 0, 0, 1, 0, 0], + # [0, 0, 0, 0, 0, 1, 0], + # [0, 0, 0, 0, 0, 0, 1], + # ] + # ), + # ) + + # self.kalman_filter.R[2:, 2:] *= 10.0 + # self.kalman_filter.P[4:, 4:] *= ( + # 1000.0 # give high uncertainty to the unobservable initial velocities + # ) + # self.kalman_filter.P *= 10.0 + # self.kalman_filter.Q[-1, -1] *= 0.01 + # self.kalman_filter.Q[4:, 4:] *= 0.01 + self.kalman_filter = KalmanFilterOCSORT(dim_x=7, dim_z=4) self.kalman_filter.R[2:, 2:] *= 10.0 self.kalman_filter.P[4:, 4:] *= ( @@ -55,6 +65,8 @@ def __init__(self, initial_bbox) -> None: self.kalman_filter.P *= 10.0 self.kalman_filter.Q[-1, -1] *= 0.01 self.kalman_filter.Q[4:, 4:] *= 0.01 + + self.kalman_filter.x[:4] = xyxy_to_xcycsr(initial_bbox).reshape((4, 1)) self.last_observation = initial_bbox # None self.previous_to_last_observation = None # For velocity of track @@ -66,17 +78,17 @@ def __init__(self, initial_bbox) -> None: self.number_of_successful_consecutive_updates = 1 # Number of frames since the last update self.time_since_update = 0 - self.save_kalman_filter_state() - - def save_kalman_filter_state(self) -> None: - """Saves the current Kalman filter state and parameters.""" - self.kalman_filter_state_before_being_lost = self.kalman_filter.state.copy() - self.kalman_filter_parameters_before_being_lost = { - "H": self.kalman_filter.H.copy(), - "Q": self.kalman_filter.Q.copy(), - "R": self.kalman_filter.R.copy(), - "P": self.kalman_filter.P.copy(), - } + # self.save_kalman_filter_state() + + # def save_kalman_filter_state(self) -> None: + # """Saves the current Kalman filter state and parameters.""" + # self.kalman_filter_state_before_being_lost = self.kalman_filter.state.copy() + # self.kalman_filter_parameters_before_being_lost = { + # "H": self.kalman_filter.H.copy(), + # "Q": self.kalman_filter.Q.copy(), + # "R": self.kalman_filter.R.copy(), + # "P": self.kalman_filter.P.copy(), + # } @classmethod def get_next_tracker_id(cls) -> int: @@ -90,16 +102,15 @@ def update(self, bbox: np.ndarray) -> None: Args: bbox (np.ndarray): The new bounding box in the form [x1, y1, x2, y2]. """ - if self.is_lost(): - self.re_update(bbox) - else: + if bbox is not None: self.kalman_filter.update(xyxy_to_xcycsr(bbox)) # save the last before being lost KF parameters for re-updating - self.save_kalman_filter_state() - self.time_since_update = 0 - self.number_of_successful_consecutive_updates += 1 - self.previous_to_last_observation = self.last_observation - self.last_observation = bbox + self.time_since_update = 0 + self.number_of_successful_consecutive_updates += 1 + self.previous_to_last_observation = self.last_observation + self.last_observation = bbox + else: + self.kalman_filter.update(None) def predict(self) -> np.ndarray: """Predicts the next bounding box position using the Kalman filter. @@ -113,8 +124,7 @@ def predict(self) -> np.ndarray: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 - predicted_bbox = self.kalman_filter.get_state_bbox() - return xcycsr_to_xyxy(predicted_bbox) + return xcycsr_to_xyxy(self.kalman_filter.x) def is_lost( self, @@ -122,37 +132,37 @@ def is_lost( """Determines if the tracklet is considered lost.""" return self.time_since_update > 1 - def re_update(self, bbox: np.ndarray) -> None: - """Re-updates the tracklet with the virtual trajectory generated out of the line that joins - the last_observation and the parameter bbox. - - Args: - bbox: The new bounding box in the form [x1, y1, x2, y2]. - """ # noqa: E501 - self.kalman_filter.state = self.kalman_filter_state_before_being_lost - self.kalman_filter.set_parameters( - **self.kalman_filter_parameters_before_being_lost - ) - bbox_xywh = xyxy_to_xywh(np.array([bbox]))[0] - last_observation_xywh = xyxy_to_xywh(np.array([self.last_observation]))[0] - for i in range(1, self.time_since_update + 1): - # Interpolate linearly between last_observation and bbox - virtual_bbox_xywh = last_observation_xywh + ( - bbox_xywh - last_observation_xywh - ) * (i / (self.time_since_update)) - virtual_bbox_xysa = np.copy(virtual_bbox_xywh) - s = virtual_bbox_xywh[2] * virtual_bbox_xywh[3] # w*h - virtual_bbox_xysa[2] = s - virtual_bbox_xysa[3] = ( - virtual_bbox_xywh[2] / virtual_bbox_xywh[3] - ) # w/h = r - - self.kalman_filter.predict() - self.kalman_filter.update(virtual_bbox_xysa) - - self.previous_to_last_observation = self.last_observation - self.last_observation = bbox - self.time_since_update = 0 + # def re_update(self, bbox: np.ndarray) -> None: + # """Re-updates the tracklet with the virtual trajectory generated out of the line that joins + # the last_observation and the parameter bbox. + + # Args: + # bbox: The new bounding box in the form [x1, y1, x2, y2]. + # """ + # self.kalman_filter.state = self.kalman_filter_state_before_being_lost + # self.kalman_filter.set_parameters( + # **self.kalman_filter_parameters_before_being_lost + # ) + # bbox_xywh = xyxy_to_xywh(np.array([bbox]))[0] + # last_observation_xywh = xyxy_to_xywh(np.array([self.last_observation]))[0] + # for i in range(1, self.time_since_update + 1): + # # Interpolate linearly between last_observation and bbox + # virtual_bbox_xywh = last_observation_xywh + ( + # bbox_xywh - last_observation_xywh + # ) * (i / (self.time_since_update)) + # virtual_bbox_xysa = np.copy(virtual_bbox_xywh) + # s = virtual_bbox_xywh[2] * virtual_bbox_xywh[3] # w*h + # virtual_bbox_xysa[2] = s + # virtual_bbox_xysa[3] = ( + # virtual_bbox_xywh[2] / virtual_bbox_xywh[3] + # ) # w/h = r + + # self.kalman_filter.predict() + # self.kalman_filter.update(virtual_bbox_xysa) + + # self.previous_to_last_observation = self.last_observation + # self.last_observation = bbox + # self.time_since_update = 0 def get_state_bbox(self) -> np.ndarray: """Returns the current bounding box estimate from the Kalman filter. @@ -160,4 +170,4 @@ def get_state_bbox(self) -> np.ndarray: Returns: np.ndarray: The current bounding box in the form [x1, y1, x2, y2]. """ - return xcycsr_to_xyxy(self.kalman_filter.get_state_bbox()) + return xcycsr_to_xyxy(self.kalman_filter.x) From 85985470988f232f85dce39bfa1286737059f03b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 30 Jan 2026 10:27:11 +0000 Subject: [PATCH 07/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- trackers/core/ocsort/kalman_filter_ocsort.py | 503 ++++++++++--------- 1 file changed, 258 insertions(+), 245 deletions(-) diff --git a/trackers/core/ocsort/kalman_filter_ocsort.py b/trackers/core/ocsort/kalman_filter_ocsort.py index 31c54177..803e6da4 100644 --- a/trackers/core/ocsort/kalman_filter_ocsort.py +++ b/trackers/core/ocsort/kalman_filter_ocsort.py @@ -92,18 +92,19 @@ from __future__ import absolute_import, division -from copy import deepcopy -from math import log, exp, sqrt import sys +from copy import deepcopy +from math import exp, log, sqrt + import numpy as np -from numpy import dot, zeros, eye, isscalar, shape import numpy.linalg as linalg -from filterpy.stats import logpdf from filterpy.common import pretty_str, reshape_z +from filterpy.stats import logpdf +from numpy import dot, eye, isscalar, shape, zeros class KalmanFilterNew(object): - """ Implements a Kalman filter. You are responsible for setting the + """Implements a Kalman filter. You are responsible for setting the various state variables to reasonable values; the defaults will not give you a functional filter. For now the best documentation is my free book Kalman and Bayesian @@ -148,7 +149,7 @@ class KalmanFilterNew(object): vector into just a position vector, so we use: .. code:: f.H = np.array([[1., 0.]]) - Define the state's covariance matrix P. + Define the state's covariance matrix P. .. code:: f.P = np.array([[1000., 0.], [ 0., 1000.] ]) @@ -278,34 +279,34 @@ class KalmanFilterNew(object): def __init__(self, dim_x, dim_z, dim_u=0): if dim_x < 1: - raise ValueError('dim_x must be 1 or greater') + raise ValueError("dim_x must be 1 or greater") if dim_z < 1: - raise ValueError('dim_z must be 1 or greater') + raise ValueError("dim_z must be 1 or greater") if dim_u < 0: - raise ValueError('dim_u must be 0 or greater') + raise ValueError("dim_u must be 0 or greater") self.dim_x = dim_x self.dim_z = dim_z self.dim_u = dim_u - self.x = zeros((dim_x, 1)) # state - self.P = eye(dim_x) # uncertainty covariance - self.Q = eye(dim_x) # process uncertainty - self.B = None # control transition matrix - self.F = eye(dim_x) # state transition matrix - self.H = zeros((dim_z, dim_x)) # measurement function - self.R = eye(dim_z) # measurement uncertainty - self._alpha_sq = 1. # fading memory control - self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation - self.z = np.array([[None]*self.dim_z]).T + self.x = zeros((dim_x, 1)) # state + self.P = eye(dim_x) # uncertainty covariance + self.Q = eye(dim_x) # process uncertainty + self.B = None # control transition matrix + self.F = eye(dim_x) # state transition matrix + self.H = zeros((dim_z, dim_x)) # measurement function + self.R = eye(dim_z) # measurement uncertainty + self._alpha_sq = 1.0 # fading memory control + self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation + self.z = np.array([[None] * self.dim_z]).T # gain and residual are computed during the innovation step. We # save them so that in case you want to inspect them for various # purposes - self.K = np.zeros((dim_x, dim_z)) # kalman gain + self.K = np.zeros((dim_x, dim_z)) # kalman gain self.y = zeros((dim_z, 1)) - self.S = np.zeros((dim_z, dim_z)) # system uncertainty - self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty + self.S = np.zeros((dim_z, dim_z)) # system uncertainty + self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty # identity matrix. Do not alter this. self._I = np.eye(dim_x) @@ -315,7 +316,7 @@ def __init__(self, dim_x, dim_z, dim_u=0): self.P_prior = self.P.copy() # these will always be a copy of x,P after update() is called - self.x_post = self.x.copy() + self.x_post = self.x.copy() self.P_post = self.P.copy() # Only computed only if requested via property @@ -323,14 +324,13 @@ def __init__(self, dim_x, dim_z, dim_u=0): self._likelihood = sys.float_info.min self._mahalanobis = None - # keep all observations + # keep all observations self.history_obs = [] self.inv = np.linalg.inv self.attr_saved = None - self.observed = False - + self.observed = False def predict(self, u=None, B=None, F=None, Q=None): """ @@ -360,7 +360,6 @@ def predict(self, u=None, B=None, F=None, Q=None): elif isscalar(Q): Q = eye(self.dim_x) * Q - # x = Fx + Bu if B is not None and u is not None: self.x = dot(F, self.x) + dot(B, u) @@ -374,62 +373,58 @@ def predict(self, u=None, B=None, F=None, Q=None): self.x_prior = self.x.copy() self.P_prior = self.P.copy() - - def freeze(self): """ - Save the parameters before non-observation forward + Save the parameters before non-observation forward """ self.attr_saved = deepcopy(self.__dict__) - def unfreeze(self): if self.attr_saved is not None: new_history = deepcopy(self.history_obs) self.__dict__ = self.attr_saved - # self.history_obs = new_history + # self.history_obs = new_history self.history_obs = self.history_obs[:-1] occur = [int(d is None) for d in new_history] - indices = np.where(np.array(occur)==0)[0] + indices = np.where(np.array(occur) == 0)[0] index1 = indices[-2] index2 = indices[-1] box1 = new_history[index1] - x1, y1, s1, r1 = box1 + x1, y1, s1, r1 = box1 w1 = np.sqrt(s1 * r1) h1 = np.sqrt(s1 / r1) box2 = new_history[index2] - x2, y2, s2, r2 = box2 + x2, y2, s2, r2 = box2 w2 = np.sqrt(s2 * r2) h2 = np.sqrt(s2 / r2) time_gap = index2 - index1 - dx = (x2-x1)/time_gap - dy = (y2-y1)/time_gap - dw = (w2-w1)/time_gap - dh = (h2-h1)/time_gap + dx = (x2 - x1) / time_gap + dy = (y2 - y1) / time_gap + dw = (w2 - w1) / time_gap + dh = (h2 - h1) / time_gap for i in range(index2 - index1): """ The default virtual trajectory generation is by linear - motion (constant speed hypothesis), you could modify this - part to implement your own. + motion (constant speed hypothesis), you could modify this + part to implement your own. """ - x = x1 + (i+1) * dx - y = y1 + (i+1) * dy - w = w1 + (i+1) * dw - h = h1 + (i+1) * dh - s = w * h + x = x1 + (i + 1) * dx + y = y1 + (i + 1) * dy + w = w1 + (i + 1) * dw + h = h1 + (i + 1) * dh + s = w * h r = w / float(h) new_box = np.array([x, y, s, r]).reshape((4, 1)) """ I still use predict-update loop here to refresh the parameters, but this can be faster by directly modifying the internal parameters - as suggested in the paper. I keep this naive but slow way for + as suggested in the paper. I keep this naive but slow way for easy read and understanding """ self.update(new_box) - if not i == (index2-index1-1): + if not i == (index2 - index1 - 1): self.predict() - def update(self, z, R=None, H=None): """ Add a new measurement (z) to the Kalman filter. @@ -457,7 +452,7 @@ def update(self, z, R=None, H=None): # append the observation self.history_obs.append(z) - + if z is None: if self.observed: """ @@ -465,13 +460,13 @@ def update(self, z, R=None, H=None): potential online smoothing. """ self.freeze() - self.observed = False - self.z = np.array([[None]*self.dim_z]).T + self.observed = False + self.z = np.array([[None] * self.dim_z]).T self.x_post = self.x.copy() self.P_post = self.P.copy() self.y = zeros((self.dim_z, 1)) return - + # self.observed = True if not self.observed: """ @@ -592,7 +587,7 @@ def update_steadystate(self, z): self._mahalanobis = None if z is None: - self.z = np.array([[None]*self.dim_z]).T + self.z = np.array([[None] * self.dim_z]).T self.x_post = self.x.copy() self.P_post = self.P.copy() self.y = zeros((self.dim_z, 1)) @@ -618,7 +613,7 @@ def update_steadystate(self, z): self._mahalanobis = None def update_correlated(self, z, R=None, H=None): - """ Add a new measurement (z) to the Kalman filter assuming that + """Add a new measurement (z) to the Kalman filter assuming that process noise and measurement noise are correlated as defined in the `self.M` matrix. A partial derivation can be found in [1] @@ -646,7 +641,7 @@ def update_correlated(self, z, R=None, H=None): self._mahalanobis = None if z is None: - self.z = np.array([[None]*self.dim_z]).T + self.z = np.array([[None] * self.dim_z]).T self.x_post = self.x.copy() self.P_post = self.P.copy() self.y = zeros((self.dim_z, 1)) @@ -667,7 +662,7 @@ def update_correlated(self, z, R=None, H=None): if self.x.ndim == 1 and shape(z) == (1, 1): z = z[0] - if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3) + if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3) z = np.asarray([z]) # y = z - Hx @@ -694,87 +689,96 @@ def update_correlated(self, z, R=None, H=None): self.x_post = self.x.copy() self.P_post = self.P.copy() - def batch_filter(self, zs, Fs=None, Qs=None, Hs=None, - Rs=None, Bs=None, us=None, update_first=False, - saver=None): - """ Batch processes a sequences of measurements. - Parameters - ---------- - zs : list-like - list of measurements at each time step `self.dt`. Missing - measurements must be represented by `None`. - Fs : None, list-like, default=None - optional value or list of values to use for the state transition - matrix F. - If Fs is None then self.F is used for all epochs. - Otherwise it must contain a list-like list of F's, one for - each epoch. This allows you to have varying F per epoch. - Qs : None, np.array or list-like, default=None - optional value or list of values to use for the process error - covariance Q. - If Qs is None then self.Q is used for all epochs. - Otherwise it must contain a list-like list of Q's, one for - each epoch. This allows you to have varying Q per epoch. - Hs : None, np.array or list-like, default=None - optional list of values to use for the measurement matrix H. - If Hs is None then self.H is used for all epochs. - If Hs contains a single matrix, then it is used as H for all - epochs. - Otherwise it must contain a list-like list of H's, one for - each epoch. This allows you to have varying H per epoch. - Rs : None, np.array or list-like, default=None - optional list of values to use for the measurement error - covariance R. - If Rs is None then self.R is used for all epochs. - Otherwise it must contain a list-like list of R's, one for - each epoch. This allows you to have varying R per epoch. - Bs : None, np.array or list-like, default=None - optional list of values to use for the control transition matrix B. - If Bs is None then self.B is used for all epochs. - Otherwise it must contain a list-like list of B's, one for - each epoch. This allows you to have varying B per epoch. - us : None, np.array or list-like, default=None - optional list of values to use for the control input vector; - If us is None then None is used for all epochs (equivalent to 0, - or no control input). - Otherwise it must contain a list-like list of u's, one for - each epoch. - update_first : bool, optional, default=False - controls whether the order of operations is update followed by - predict, or predict followed by update. Default is predict->update. - saver : filterpy.common.Saver, optional - filterpy.common.Saver object. If provided, saver.save() will be - called after every epoch - Returns - ------- - means : np.array((n,dim_x,1)) - array of the state for each time step after the update. Each entry - is an np.array. In other words `means[k,:]` is the state at step - `k`. - covariance : np.array((n,dim_x,dim_x)) - array of the covariances for each time step after the update. - In other words `covariance[k,:,:]` is the covariance at step `k`. - means_predictions : np.array((n,dim_x,1)) - array of the state for each time step after the predictions. Each - entry is an np.array. In other words `means[k,:]` is the state at - step `k`. - covariance_predictions : np.array((n,dim_x,dim_x)) - array of the covariances for each time step after the prediction. - In other words `covariance[k,:,:]` is the covariance at step `k`. - Examples - -------- - .. code-block:: Python - # this example demonstrates tracking a measurement where the time - # between measurement varies, as stored in dts. This requires - # that F be recomputed for each epoch. The output is then smoothed - # with an RTS smoother. - zs = [t + random.randn()*4 for t in range (40)] - Fs = [np.array([[1., dt], [0, 1]] for dt in dts] - (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs) - (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs) + def batch_filter( + self, + zs, + Fs=None, + Qs=None, + Hs=None, + Rs=None, + Bs=None, + us=None, + update_first=False, + saver=None, + ): + """Batch processes a sequences of measurements. + Parameters + ---------- + zs : list-like + list of measurements at each time step `self.dt`. Missing + measurements must be represented by `None`. + Fs : None, list-like, default=None + optional value or list of values to use for the state transition + matrix F. + If Fs is None then self.F is used for all epochs. + Otherwise it must contain a list-like list of F's, one for + each epoch. This allows you to have varying F per epoch. + Qs : None, np.array or list-like, default=None + optional value or list of values to use for the process error + covariance Q. + If Qs is None then self.Q is used for all epochs. + Otherwise it must contain a list-like list of Q's, one for + each epoch. This allows you to have varying Q per epoch. + Hs : None, np.array or list-like, default=None + optional list of values to use for the measurement matrix H. + If Hs is None then self.H is used for all epochs. + If Hs contains a single matrix, then it is used as H for all + epochs. + Otherwise it must contain a list-like list of H's, one for + each epoch. This allows you to have varying H per epoch. + Rs : None, np.array or list-like, default=None + optional list of values to use for the measurement error + covariance R. + If Rs is None then self.R is used for all epochs. + Otherwise it must contain a list-like list of R's, one for + each epoch. This allows you to have varying R per epoch. + Bs : None, np.array or list-like, default=None + optional list of values to use for the control transition matrix B. + If Bs is None then self.B is used for all epochs. + Otherwise it must contain a list-like list of B's, one for + each epoch. This allows you to have varying B per epoch. + us : None, np.array or list-like, default=None + optional list of values to use for the control input vector; + If us is None then None is used for all epochs (equivalent to 0, + or no control input). + Otherwise it must contain a list-like list of u's, one for + each epoch. + update_first : bool, optional, default=False + controls whether the order of operations is update followed by + predict, or predict followed by update. Default is predict->update. + saver : filterpy.common.Saver, optional + filterpy.common.Saver object. If provided, saver.save() will be + called after every epoch + Returns + ------- + means : np.array((n,dim_x,1)) + array of the state for each time step after the update. Each entry + is an np.array. In other words `means[k,:]` is the state at step + `k`. + covariance : np.array((n,dim_x,dim_x)) + array of the covariances for each time step after the update. + In other words `covariance[k,:,:]` is the covariance at step `k`. + means_predictions : np.array((n,dim_x,1)) + array of the state for each time step after the predictions. Each + entry is an np.array. In other words `means[k,:]` is the state at + step `k`. + covariance_predictions : np.array((n,dim_x,dim_x)) + array of the covariances for each time step after the prediction. + In other words `covariance[k,:,:]` is the covariance at step `k`. + Examples + -------- + .. code-block:: Python + # this example demonstrates tracking a measurement where the time + # between measurement varies, as stored in dts. This requires + # that F be recomputed for each epoch. The output is then smoothed + # with an RTS smoother. + zs = [t + random.randn()*4 for t in range (40)] + Fs = [np.array([[1., dt], [0, 1]] for dt in dts] + (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs) + (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs) """ - #pylint: disable=too-many-statements + # pylint: disable=too-many-statements n = np.size(zs, 0) if Fs is None: Fs = [self.F] * n @@ -803,7 +807,6 @@ def batch_filter(self, zs, Fs=None, Qs=None, Hs=None, if update_first: for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): - self.update(z, R=R, H=H) means[i, :] = self.x covariances[i, :, :] = self.P @@ -816,7 +819,6 @@ def batch_filter(self, zs, Fs=None, Qs=None, Hs=None, saver.save() else: for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): - self.predict(u=u, B=B, F=F, Q=Q) means_p[i, :] = self.x covariances_p[i, :, :] = self.P @@ -870,7 +872,7 @@ def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv): """ if len(Xs) != len(Ps): - raise ValueError('length of Xs and Ps must be the same') + raise ValueError("length of Xs and Ps must be the same") n = Xs.shape[0] dim_x = Xs.shape[1] @@ -884,13 +886,13 @@ def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv): K = zeros((n, dim_x, dim_x)) x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy() - for k in range(n-2, -1, -1): - Pp[k] = dot(dot(Fs[k+1], P[k]), Fs[k+1].T) + Qs[k+1] + for k in range(n - 2, -1, -1): + Pp[k] = dot(dot(Fs[k + 1], P[k]), Fs[k + 1].T) + Qs[k + 1] - #pylint: disable=bad-whitespace - K[k] = dot(dot(P[k], Fs[k+1].T), inv(Pp[k])) - x[k] += dot(K[k], x[k+1] - dot(Fs[k+1], x[k])) - P[k] += dot(dot(K[k], P[k+1] - Pp[k]), K[k].T) + # pylint: disable=bad-whitespace + K[k] = dot(dot(P[k], Fs[k + 1].T), inv(Pp[k])) + x[k] += dot(K[k], x[k + 1] - dot(Fs[k + 1], x[k])) + P[k] += dot(dot(K[k], P[k + 1] - Pp[k]), K[k].T) return (x, P, K, Pp) @@ -950,7 +952,7 @@ def get_update(self, z=None): ------- (x, P) : tuple State vector and covariance array of the update. - """ + """ if z is None: return self.x, self.P @@ -1032,7 +1034,7 @@ def likelihood(self): @property def mahalanobis(self): - """" + """ " Mahalanobis distance of measurement. E.g. 3 means measurement was 3 standard deviations away from the predicted value. Returns @@ -1052,7 +1054,7 @@ def alpha(self): filter's estimates. This formulation of the Fading memory filter (there are many) is due to Dan Simon [1]_. """ - return self._alpha_sq**.5 + return self._alpha_sq**0.5 def log_likelihood_of(self, z): """ @@ -1067,39 +1069,41 @@ def log_likelihood_of(self, z): @alpha.setter def alpha(self, value): if not np.isscalar(value) or value < 1: - raise ValueError('alpha must be a float greater than 1') + raise ValueError("alpha must be a float greater than 1") self._alpha_sq = value**2 def __repr__(self): - return '\n'.join([ - 'KalmanFilter object', - pretty_str('dim_x', self.dim_x), - pretty_str('dim_z', self.dim_z), - pretty_str('dim_u', self.dim_u), - pretty_str('x', self.x), - pretty_str('P', self.P), - pretty_str('x_prior', self.x_prior), - pretty_str('P_prior', self.P_prior), - pretty_str('x_post', self.x_post), - pretty_str('P_post', self.P_post), - pretty_str('F', self.F), - pretty_str('Q', self.Q), - pretty_str('R', self.R), - pretty_str('H', self.H), - pretty_str('K', self.K), - pretty_str('y', self.y), - pretty_str('S', self.S), - pretty_str('SI', self.SI), - pretty_str('M', self.M), - pretty_str('B', self.B), - pretty_str('z', self.z), - pretty_str('log-likelihood', self.log_likelihood), - pretty_str('likelihood', self.likelihood), - pretty_str('mahalanobis', self.mahalanobis), - pretty_str('alpha', self.alpha), - pretty_str('inv', self.inv) - ]) + return "\n".join( + [ + "KalmanFilter object", + pretty_str("dim_x", self.dim_x), + pretty_str("dim_z", self.dim_z), + pretty_str("dim_u", self.dim_u), + pretty_str("x", self.x), + pretty_str("P", self.P), + pretty_str("x_prior", self.x_prior), + pretty_str("P_prior", self.P_prior), + pretty_str("x_post", self.x_post), + pretty_str("P_post", self.P_post), + pretty_str("F", self.F), + pretty_str("Q", self.Q), + pretty_str("R", self.R), + pretty_str("H", self.H), + pretty_str("K", self.K), + pretty_str("y", self.y), + pretty_str("S", self.S), + pretty_str("SI", self.SI), + pretty_str("M", self.M), + pretty_str("B", self.B), + pretty_str("z", self.z), + pretty_str("log-likelihood", self.log_likelihood), + pretty_str("likelihood", self.likelihood), + pretty_str("mahalanobis", self.mahalanobis), + pretty_str("alpha", self.alpha), + pretty_str("inv", self.inv), + ] + ) def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None): """ @@ -1127,37 +1131,44 @@ def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None): x = self.x P = self.P - assert x.ndim == 1 or x.ndim == 2, \ - "x must have one or two dimensions, but has {}".format(x.ndim) + assert x.ndim == 1 or x.ndim == 2, ( + "x must have one or two dimensions, but has {}".format(x.ndim) + ) if x.ndim == 1: - assert x.shape[0] == self.dim_x, \ - "Shape of x must be ({},{}), but is {}".format( - self.dim_x, 1, x.shape) + assert x.shape[0] == self.dim_x, ( + "Shape of x must be ({},{}), but is {}".format(self.dim_x, 1, x.shape) + ) else: - assert x.shape == (self.dim_x, 1), \ - "Shape of x must be ({},{}), but is {}".format( - self.dim_x, 1, x.shape) - - assert P.shape == (self.dim_x, self.dim_x), \ - "Shape of P must be ({},{}), but is {}".format( - self.dim_x, self.dim_x, P.shape) - - assert Q.shape == (self.dim_x, self.dim_x), \ - "Shape of Q must be ({},{}), but is {}".format( - self.dim_x, self.dim_x, P.shape) - - assert F.shape == (self.dim_x, self.dim_x), \ - "Shape of F must be ({},{}), but is {}".format( - self.dim_x, self.dim_x, F.shape) - - assert np.ndim(H) == 2, \ - "Shape of H must be (dim_z, {}), but is {}".format( - P.shape[0], shape(H)) - - assert H.shape[1] == P.shape[0], \ - "Shape of H must be (dim_z, {}), but is {}".format( - P.shape[0], H.shape) + assert x.shape == (self.dim_x, 1), ( + "Shape of x must be ({},{}), but is {}".format(self.dim_x, 1, x.shape) + ) + + assert P.shape == (self.dim_x, self.dim_x), ( + "Shape of P must be ({},{}), but is {}".format( + self.dim_x, self.dim_x, P.shape + ) + ) + + assert Q.shape == (self.dim_x, self.dim_x), ( + "Shape of Q must be ({},{}), but is {}".format( + self.dim_x, self.dim_x, P.shape + ) + ) + + assert F.shape == (self.dim_x, self.dim_x), ( + "Shape of F must be ({},{}), but is {}".format( + self.dim_x, self.dim_x, F.shape + ) + ) + + assert np.ndim(H) == 2, "Shape of H must be (dim_z, {}), but is {}".format( + P.shape[0], shape(H) + ) + + assert H.shape[1] == P.shape[0], ( + "Shape of H must be (dim_z, {}), but is {}".format(P.shape[0], H.shape) + ) # shape of R must be the same as HPH' hph_shape = (H.shape[0], H.shape[0]) @@ -1165,13 +1176,15 @@ def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None): if H.shape[0] == 1: # r can be scalar, 1D, or 2D in this case - assert r_shape in [(), (1,), (1, 1)], \ - "R must be scalar or one element array, but is shaped {}".format( - r_shape) + assert r_shape in [(), (1,), (1, 1)], ( + "R must be scalar or one element array, but is shaped {}".format( + r_shape + ) + ) else: - assert r_shape == hph_shape, \ - "shape of R should be {} but it is {}".format(hph_shape, r_shape) - + assert r_shape == hph_shape, "shape of R should be {} but it is {}".format( + hph_shape, r_shape + ) if z is not None: z_shape = shape(z) @@ -1181,24 +1194,31 @@ def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None): # H@x must have shape of z Hx = dot(H, x) - if z_shape == (): # scalar or np.array(scalar) - assert Hx.ndim == 1 or shape(Hx) == (1, 1), \ - "shape of z should be {}, not {} for the given H".format( - shape(Hx), z_shape) + if z_shape == (): # scalar or np.array(scalar) + assert Hx.ndim == 1 or shape(Hx) == (1, 1), ( + "shape of z should be {}, not {} for the given H".format( + shape(Hx), z_shape + ) + ) elif shape(Hx) == (1,): - assert z_shape[0] == 1, 'Shape of z must be {} for the given H'.format(shape(Hx)) + assert z_shape[0] == 1, "Shape of z must be {} for the given H".format( + shape(Hx) + ) else: - assert (z_shape == shape(Hx) or - (len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1))), \ - "shape of z should be {}, not {} for the given H".format( - shape(Hx), z_shape) + assert z_shape == shape(Hx) or ( + len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1) + ), "shape of z should be {}, not {} for the given H".format( + shape(Hx), z_shape + ) if np.ndim(Hx) > 1 and shape(Hx) != (1, 1): - assert shape(Hx) == z_shape, \ - 'shape of z should be {} for the given H, but it is {}'.format( - shape(Hx), z_shape) + assert shape(Hx) == z_shape, ( + "shape of z should be {} for the given H, but it is {}".format( + shape(Hx), z_shape + ) + ) def update(x, P, z, R, H=None, return_all=False): @@ -1242,7 +1262,7 @@ def update(x, P, z, R, H=None, return_all=False): log likelihood of the measurement """ - #pylint: disable=bare-except + # pylint: disable=bare-except if z is None: if return_all: @@ -1264,14 +1284,12 @@ def update(x, P, z, R, H=None, return_all=False): # project system uncertainty into measurement space S = dot(dot(H, P), H.T) + R - # map system uncertainty into kalman gain try: K = dot(dot(P, H.T), linalg.inv(S)) except: # can't invert a 1D array, annoyingly - K = dot(dot(P, H.T), 1./S) - + K = dot(dot(P, H.T), 1.0 / S) # predict new x with residual scaled by the kalman gain x = x + dot(K, y) @@ -1285,7 +1303,6 @@ def update(x, P, z, R, H=None, return_all=False): I_KH = np.array([1 - KH]) P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T) - if return_all: # compute log likelihood log_likelihood = logpdf(z, dot(H, x), S) @@ -1321,7 +1338,6 @@ def update_steadystate(x, z, K, H=None): >>> update_steadystate(x, P, z, H) """ - if z is None: return x @@ -1341,7 +1357,7 @@ def update_steadystate(x, z, K, H=None): return x + dot(K, y) -def predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.): +def predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.0): """ Predict next state (prior) using the Kalman filter state propagation equations. @@ -1413,9 +1429,9 @@ def predict_steadystate(x, F=1, u=0, B=1): return x - -def batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None, - update_first=False, saver=None): +def batch_filter( + x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None, update_first=False, saver=None +): """ Batch processes a sequences of measurements. Parameters @@ -1490,12 +1506,11 @@ def batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None, covariances_p = zeros((n, dim_x, dim_x)) if us is None: - us = [0.] * n - Bs = [0.] * n + us = [0.0] * n + Bs = [0.0] * n if update_first: for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): - x, P = update(x, P, z, R=R, H=H) means[i, :] = x covariances[i, :, :] = P @@ -1507,7 +1522,6 @@ def batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None, saver.save() else: for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): - x, P = predict(x, P, u=u, B=B, F=F, Q=Q) means_p[i, :] = x covariances_p[i, :, :] = P @@ -1521,7 +1535,6 @@ def batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None, return (means, covariances, means_p, covariances_p) - def rts_smoother(Xs, Ps, Fs, Qs): """ Runs the Rauch-Tung-Striebel Kalman smoother on a set of @@ -1557,7 +1570,7 @@ def rts_smoother(Xs, Ps, Fs, Qs): """ if len(Xs) != len(Ps): - raise ValueError('length of Xs and Ps must be the same') + raise ValueError("length of Xs and Ps must be the same") n = Xs.shape[0] dim_x = Xs.shape[1] @@ -1566,12 +1579,12 @@ def rts_smoother(Xs, Ps, Fs, Qs): K = zeros((n, dim_x, dim_x)) x, P, pP = Xs.copy(), Ps.copy(), Ps.copy() - for k in range(n-2, -1, -1): + for k in range(n - 2, -1, -1): pP[k] = dot(dot(Fs[k], P[k]), Fs[k].T) + Qs[k] - #pylint: disable=bad-whitespace - K[k] = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k])) - x[k] += dot(K[k], x[k+1] - dot(Fs[k], x[k])) - P[k] += dot(dot(K[k], P[k+1] - pP[k]), K[k].T) + # pylint: disable=bad-whitespace + K[k] = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k])) + x[k] += dot(K[k], x[k + 1] - dot(Fs[k], x[k])) + P[k] += dot(dot(K[k], P[k + 1] - pP[k]), K[k].T) - return (x, P, K, pP) \ No newline at end of file + return (x, P, K, pP) From e1531838d1a9db4c7b9e0d7a8587f8d0a2a57559 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 30 Jan 2026 12:05:57 +0100 Subject: [PATCH 08/87] trying fix --- trackers/core/ocsort/tracklet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 716e7bb8..a516892f 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -124,7 +124,7 @@ def predict(self) -> np.ndarray: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 - return xcycsr_to_xyxy(self.kalman_filter.x) + return xcycsr_to_xyxy(self.kalman_filter.x.reshape((4))) def is_lost( self, @@ -170,4 +170,4 @@ def get_state_bbox(self) -> np.ndarray: Returns: np.ndarray: The current bounding box in the form [x1, y1, x2, y2]. """ - return xcycsr_to_xyxy(self.kalman_filter.x) + return xcycsr_to_xyxy(self.kalman_filter.x.reshape((4))) From 0db6ab1df2ee681ad8b61ab150866dc620f8d275 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 30 Jan 2026 12:13:06 +0100 Subject: [PATCH 09/87] trying fix --- trackers/core/ocsort/tracklet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index a516892f..4e558d8f 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -124,7 +124,7 @@ def predict(self) -> np.ndarray: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 - return xcycsr_to_xyxy(self.kalman_filter.x.reshape((4))) + return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4))) def is_lost( self, @@ -170,4 +170,4 @@ def get_state_bbox(self) -> np.ndarray: Returns: np.ndarray: The current bounding box in the form [x1, y1, x2, y2]. """ - return xcycsr_to_xyxy(self.kalman_filter.x.reshape((4))) + return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4))) From c6e2fb09d073bb627b2bc88b03d6c768ba53ec66 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 30 Jan 2026 12:25:03 +0100 Subject: [PATCH 10/87] trying fix --- trackers/core/ocsort/tracker.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index d820d8de..c0390b07 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -207,6 +207,11 @@ def update(self, detections: sv.Detections) -> sv.Detections: updated_detections, ) + # Update OCR-unmatched tracks with None before filtering; indices + # refer to current self.tracks. + for m in _ocr_unmatched_tracks: + self.tracks[unmatched_tracks[m]].update(None) + self.tracks = self.activate_or_kill_tracklets() self._spawn_new_tracklets( detections[unmatched_detections], ocr_unmatched_detections @@ -216,8 +221,6 @@ def update(self, detections: sv.Detections) -> sv.Detections: [-1] * len(left_detections), dtype=int ) updated_detections.append(left_detections) - for m in _ocr_unmatched_tracks: - self.tracks[unmatched_tracks[m]].update(None) else: self.tracks = self.activate_or_kill_tracklets() self._spawn_new_tracklets(detections, unmatched_detections) From 03faa9348ffa92b3fe80ac2b64cbbab67f702991 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 30 Jan 2026 15:04:43 +0100 Subject: [PATCH 11/87] added F initialization --- trackers/core/ocsort/tracklet.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 4e558d8f..ddbf0f9c 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -58,6 +58,30 @@ def __init__(self, initial_bbox) -> None: # self.kalman_filter.Q[4:, 4:] *= 0.01 self.kalman_filter = KalmanFilterOCSORT(dim_x=7, dim_z=4) + # State (x,y,s,r,vx,vy,vs): constant velocity model + self.kalman_filter.F = np.array( + [ + [1, 0, 0, 0, 1, 0, 0], + [0, 1, 0, 0, 0, 1, 0], + [0, 0, 1, 0, 0, 0, 1], + [0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 0, 1], + ], + dtype=np.float64, + ) + # Observe (x, y, s, r) from state + self.kalman_filter.H = np.array( + [ + [1, 0, 0, 0, 0, 0, 0], + [0, 1, 0, 0, 0, 0, 0], + [0, 0, 1, 0, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0], + ], + dtype=np.float64, + ) + self.kalman_filter.R[2:, 2:] *= 10.0 self.kalman_filter.P[4:, 4:] *= ( 1000.0 # give high uncertainty to the unobservable initial velocities From d16ecfbb2836cc6a47e3d90bf4303d40ded527f6 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 5 Feb 2026 13:21:21 -0300 Subject: [PATCH 12/87] possible fix by transforming cosine_sim --- trackers/core/ocsort/utils.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index df330821..c03175a4 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -50,7 +50,11 @@ def build_direction_consistency_matrix( # Compute cosine similarity cos_sim = np.dot(tracklet_speed, association_speed) - direction_consistency_matrix[t, d] = cos_sim + cos_sim = np.clip(cos_sim, -1.0, 1.0) + # Apply the same transformation as original + angle = np.arccos(cos_sim) + direction_consistency_matrix[t, d] = (np.pi / 2.0 - np.abs(angle)) / np.pi + #direction_consistency_matrix[t, d] = cos_sim return direction_consistency_matrix From 81fed515c1bf7d23fe3d43443b2c79685e05b96c Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 5 Feb 2026 14:16:51 -0300 Subject: [PATCH 13/87] changed indexing fro initializing new tracklets when ocr unmatched and update also when unmatched detections = 0 --- trackers/core/ocsort/tracker.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index c0390b07..ecae5fc6 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -216,12 +216,16 @@ def update(self, detections: sv.Detections) -> sv.Detections: self._spawn_new_tracklets( detections[unmatched_detections], ocr_unmatched_detections ) - left_detections = detections[ocr_unmatched_detections] + left_detections = detections[unmatched_detections][ + ocr_unmatched_detections + ] left_detections.tracker_id = np.array( [-1] * len(left_detections), dtype=int ) updated_detections.append(left_detections) else: + for track_idx in unmatched_tracks: + self.tracks[track_idx].update(None) self.tracks = self.activate_or_kill_tracklets() self._spawn_new_tracklets(detections, unmatched_detections) left_detections = detections[unmatched_detections] From fa42061d78970b47d40b7ead22d0e1f5bac639d5 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 5 Feb 2026 14:49:39 -0300 Subject: [PATCH 14/87] now in frames before minimum consecutive frames we output an id --- trackers/core/ocsort/tracker.py | 15 +++++++++++---- trackers/core/ocsort/utils.py | 14 +++++++++++++- 2 files changed, 24 insertions(+), 5 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index ecae5fc6..f761a588 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -67,7 +67,7 @@ def __init__( self.high_conf_det_threshold = high_conf_det_threshold # Active tracks self.tracks: list[OCSORTTracklet] = [] - + self.frame_count = 0 def _get_associated_indices( self, iou_matrix: np.ndarray, @@ -176,7 +176,8 @@ def update(self, detections: sv.Detections) -> sv.Detections: for row, col in matched_indices: self.tracks[row].update(detection_boxes[col]) add_track_id_detections( - self.tracks[row], detections[col : col + 1], updated_detections + self.tracks[row], detections[col : col + 1], updated_detections, + self.minimum_consecutive_frames, self.frame_count ) # Run 2nd Chance Association (OCR) @@ -205,6 +206,8 @@ def update(self, detections: sv.Detections) -> sv.Detections: self.tracks[track_idx], detections[det_idx : det_idx + 1], updated_detections, + self.minimum_consecutive_frames, + self.frame_count, ) # Update OCR-unmatched tracks with None before filtering; indices @@ -236,6 +239,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: final_updated_detections = sv.Detections.merge(updated_detections) if len(final_updated_detections) == 0: final_updated_detections.tracker_id = np.array([], dtype=int) + frame_count += 1 return final_updated_detections def reset(self) -> None: @@ -244,6 +248,7 @@ def reset(self) -> None: Clears all active tracks and resets the track ID counter. """ self.tracks = [] + self.frame_count = 0 OCSORTTracklet.count_id = 0 def activate_or_kill_tracklets(self): @@ -263,7 +268,9 @@ def activate_or_kill_tracklets(self): # is_active = tracklet.time_since_update == 0 if tracklet.time_since_update <= self.maximum_frames_without_update: alive_tracklets.append(tracklet) - - if is_mature and tracklet.tracker_id == -1: + if self.frame_count < self.minimum_consecutive_frames: tracklet.tracker_id = OCSORTTracklet.get_next_tracker_id() + else: + if is_mature and tracklet.tracker_id == -1: + tracklet.tracker_id = OCSORTTracklet.get_next_tracker_id() return alive_tracklets diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index c03175a4..1a30fae1 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -117,6 +117,8 @@ def add_track_id_detections( track: OCSORTTracklet, detection: sv.Detections, updated_detections: list[sv.Detections], + minimum_consecutive_frames: int, + frame_count: int, ) -> sv.Detections: """ The function prepares the updated Detections with track IDs. @@ -127,6 +129,8 @@ def add_track_id_detections( detections: The latest set of object detections. tracklets: List of OCSORTTracklet objects. updated_detections: List of detections in which we add detections with assigned track IDs. + minimum_consecutive_frames: The number of consecutive frames required for a track to be considered "mature". + frame_count: The current frame count in the tracking process. Returns: sv.Detections: A copy of the detections with `tracker_id` set for each detection that is tracked. @@ -134,7 +138,15 @@ def add_track_id_detections( new_det = deepcopy(detection) # Add cast to clarify type for mypy new_det = cast(sv.Detections, new_det) # ADDED cast - new_det.tracker_id = np.array([track.tracker_id]) + is_mature = ( + track.number_of_successful_consecutive_updates + >= minimum_consecutive_frames + ) + + if (frame_count >= minimum_consecutive_frames and is_mature) or frame_count < minimum_consecutive_frames: + new_det.tracker_id = np.array([track.tracker_id]) + else: + new_det.tracker_id = np.array([-1], dtype=int) updated_detections.append(new_det) From f801c783e0b1d6e574495d66b24080eff10332a7 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 5 Feb 2026 14:54:15 -0300 Subject: [PATCH 15/87] now in frames before minimum consecutive frames we output an id --- trackers/core/ocsort/tracker.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index f761a588..e9bdc72e 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -239,7 +239,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: final_updated_detections = sv.Detections.merge(updated_detections) if len(final_updated_detections) == 0: final_updated_detections.tracker_id = np.array([], dtype=int) - frame_count += 1 + self.frame_count += 1 return final_updated_detections def reset(self) -> None: From 457841c725153999f1bdda1df46e330d99d79b2d Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 5 Feb 2026 15:22:30 -0300 Subject: [PATCH 16/87] now in frames before minimum consecutive frames we output an id --- trackers/core/ocsort/tracker.py | 6 ++---- trackers/core/ocsort/utils.py | 8 +++++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index e9bdc72e..5d2879c2 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -268,9 +268,7 @@ def activate_or_kill_tracklets(self): # is_active = tracklet.time_since_update == 0 if tracklet.time_since_update <= self.maximum_frames_without_update: alive_tracklets.append(tracklet) - if self.frame_count < self.minimum_consecutive_frames: + + if is_mature and tracklet.tracker_id == -1: tracklet.tracker_id = OCSORTTracklet.get_next_tracker_id() - else: - if is_mature and tracklet.tracker_id == -1: - tracklet.tracker_id = OCSORTTracklet.get_next_tracker_id() return alive_tracklets diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 1a30fae1..bebd310f 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -118,7 +118,7 @@ def add_track_id_detections( detection: sv.Detections, updated_detections: list[sv.Detections], minimum_consecutive_frames: int, - frame_count: int, + frame_count: int ) -> sv.Detections: """ The function prepares the updated Detections with track IDs. @@ -142,8 +142,10 @@ def add_track_id_detections( track.number_of_successful_consecutive_updates >= minimum_consecutive_frames ) - - if (frame_count >= minimum_consecutive_frames and is_mature) or frame_count < minimum_consecutive_frames: + if frame_count < minimum_consecutive_frames and track.time_since_update == 0: + track.tracker_id = OCSORTTracklet.get_next_tracker_id() + + if (frame_count >= minimum_consecutive_frames and is_mature) : new_det.tracker_id = np.array([track.tracker_id]) else: new_det.tracker_id = np.array([-1], dtype=int) From e02996afe0e863dd00443a0e1ff56f1b2e45a827 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 6 Feb 2026 11:17:08 -0300 Subject: [PATCH 17/87] bug fix regarding last commits --- trackers/core/ocsort/utils.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index bebd310f..8f94b15a 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -142,13 +142,15 @@ def add_track_id_detections( track.number_of_successful_consecutive_updates >= minimum_consecutive_frames ) - if frame_count < minimum_consecutive_frames and track.time_since_update == 0: - track.tracker_id = OCSORTTracklet.get_next_tracker_id() - - if (frame_count >= minimum_consecutive_frames and is_mature) : - new_det.tracker_id = np.array([track.tracker_id]) - else: - new_det.tracker_id = np.array([-1], dtype=int) + if frame_count < minimum_consecutive_frames : + if track.time_since_update == 0: + track.tracker_id = OCSORTTracklet.get_next_tracker_id() + new_det.tracker_id = np.array([track.tracker_id]) + else: + if is_mature : + new_det.tracker_id = np.array([track.tracker_id]) + else: + new_det.tracker_id = np.array([-1], dtype=int) updated_detections.append(new_det) From 25e6f5a03e25ce9ab28e83cce895e57df28d2ae2 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 6 Feb 2026 11:46:05 -0300 Subject: [PATCH 18/87] bug fix regarding last commits --- trackers/core/ocsort/utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 8f94b15a..b312bb03 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -144,7 +144,9 @@ def add_track_id_detections( ) if frame_count < minimum_consecutive_frames : if track.time_since_update == 0: - track.tracker_id = OCSORTTracklet.get_next_tracker_id() + if track.tracker_id == -1: + track.tracker_id = OCSORTTracklet.get_next_tracker_id() + new_det.tracker_id = np.array([track.tracker_id]) else: if is_mature : From ac1b49085071a76eff16c048d67c50a4e3370efe Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 6 Feb 2026 12:42:40 -0300 Subject: [PATCH 19/87] possbile fix by activating track id when just matured --- trackers/core/ocsort/utils.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index b312bb03..1e633df9 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -142,14 +142,17 @@ def add_track_id_detections( track.number_of_successful_consecutive_updates >= minimum_consecutive_frames ) - if frame_count < minimum_consecutive_frames : + if frame_count < minimum_consecutive_frames: if track.time_since_update == 0: - if track.tracker_id == -1: + if track.tracker_id == -1: track.tracker_id = OCSORTTracklet.get_next_tracker_id() new_det.tracker_id = np.array([track.tracker_id]) - else: - if is_mature : + else: + if is_mature: + # Assign ID now if track just became mature + if track.tracker_id == -1: + track.tracker_id = OCSORTTracklet.get_next_tracker_id() new_det.tracker_id = np.array([track.tracker_id]) else: new_det.tracker_id = np.array([-1], dtype=int) From 89de4767ac882ae2e101034eab6a7046ce512b16 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 6 Feb 2026 14:58:10 -0300 Subject: [PATCH 20/87] now doing batch calculation of direction consistency, faster tracking --- trackers/core/ocsort/tracker.py | 4 +-- trackers/core/ocsort/utils.py | 56 +++++++++++++++++++++------------ 2 files changed, 38 insertions(+), 22 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 5d2879c2..96bd055c 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -6,7 +6,7 @@ from trackers.core.ocsort.tracklet import OCSORTTracklet from trackers.core.ocsort.utils import ( add_track_id_detections, - build_direction_consistency_matrix, + build_direction_consistency_matrix_batch, get_iou_matrix, get_iou_matrix_between_boxes, ) @@ -162,7 +162,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: # Build IOU cost matrix between detections and predicted bounding boxes iou_matrix = get_iou_matrix(self.tracks, detection_boxes) - direction_consistency_matrix = build_direction_consistency_matrix( + direction_consistency_matrix = build_direction_consistency_matrix_batch( self.tracks, detection_boxes ) direction_consistency_matrix *= detections.confidence[np.newaxis, :] diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 1e633df9..ab250dbc 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -77,40 +77,56 @@ def speed_direction_batch(dets, tracks): # From oc sort repo def build_direction_consistency_matrix_batch( - # vectorized version from oc sort repo but adapted to our tracklet class, still needs testing and is not currently used# noqa: E501 + # vectorized version from oc sort repo but adapted to our tracklet class tracklets: list[OCSORTTracklet], detection_boxes: np.ndarray, ) -> np.ndarray: + n_tracklets = len(tracklets) + n_detections = detection_boxes.shape[0] if len(detection_boxes) > 0 else 0 + + if n_tracklets == 0 or n_detections == 0: + return np.zeros((n_tracklets, n_detections), dtype=np.float32) + + # Compute tracklet velocities (from previous_to_last -> last_observation) velocities = np.array( [ speed_direction( tracklet.previous_to_last_observation, tracklet.last_observation ) if tracklet.previous_to_last_observation is not None - else (0, 0) + else np.array([0.0, 0.0]) for tracklet in tracklets ] - ) - Y, X = speed_direction_batch(detection_boxes, tracklets) # these will break - inertia_Y, inertia_X = velocities[:, 0], velocities[:, 1] - inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1) - inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1) - diff_angle_cos = inertia_X * X + inertia_Y * Y - diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1) + ) # shape: (n_tracklets, 2) where each row is [dy, dx] + + # Get last observations as array for batch direction computation + last_obs = np.array([tracklet.last_observation for tracklet in tracklets]) + + # Compute association directions (from last_observation -> detection) in batch + # speed_direction_batch expects (dets, tracks) and returns (dy, dx) each of shape (n_tracks, n_dets) + Y, X = speed_direction_batch(detection_boxes, last_obs) # (n_tracklets, n_detections) + + # Expand velocities for broadcasting + inertia_Y = velocities[:, 0:1] # (n_tracklets, 1) + inertia_X = velocities[:, 1:2] # (n_tracklets, 1) + + # Compute cosine similarity (dot product of normalized vectors) + diff_angle_cos = inertia_X * X + inertia_Y * Y # (n_tracklets, n_detections) + diff_angle_cos = np.clip(diff_angle_cos, -1.0, 1.0) + + # Apply same transformation as non-batch version diff_angle = np.arccos(diff_angle_cos) - diff_angle = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi + angle_diff_cost = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi - valid_mask = np.ones(len(tracklets)) - # valid_mask[np.where(previous_obs[:,4]<0)] = 0 - previous_to_last_observation_is_none = np.array( - [tracklet.previous_to_last_observation is None for tracklet in tracklets] - ) - valid_mask[previous_to_last_observation_is_none] = 0 - valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1) + # Mask out tracklets without previous observation + valid_mask = np.array( + [tracklet.previous_to_last_observation is not None for tracklet in tracklets], + dtype=np.float32 + )[:, np.newaxis] # (n_tracklets, 1) + + angle_diff_cost = valid_mask * angle_diff_cost - angle_diff_cost = valid_mask * diff_angle - angle_diff_cost = angle_diff_cost.T # check if this transpose is needed - return angle_diff_cost + return angle_diff_cost.astype(np.float32) def add_track_id_detections( From bc74315d916668ec10481f743e83969bd59993de Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 6 Feb 2026 15:26:38 -0300 Subject: [PATCH 21/87] nice docs in utils --- trackers/core/ocsort/utils.py | 143 ++++++++++++++++++++-------------- 1 file changed, 86 insertions(+), 57 deletions(-) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index ab250dbc..5fe7d49d 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -16,7 +16,16 @@ from trackers.core.ocsort.tracklet import OCSORTTracklet -def speed_direction(bbox1, bbox2): # from oc sort repo +def speed_direction(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: + """Compute normalized direction vector between two bounding box centers. + + Args: + bbox1: First bounding box in the form [x1, y1, x2, y2]. + bbox2: Second bounding box in the form [x1, y1, x2, y2]. + + Returns: + np.ndarray: Normalized direction vector [dy, dx] from bbox1 to bbox2. + """ cx1, cy1 = (bbox1[0] + bbox1[2]) / 2.0, (bbox1[1] + bbox1[3]) / 2.0 cx2, cy2 = (bbox2[0] + bbox2[2]) / 2.0, (bbox2[1] + bbox2[3]) / 2.0 speed = np.array([cy2 - cy1, cx2 - cx1]) @@ -25,10 +34,22 @@ def speed_direction(bbox1, bbox2): # from oc sort repo def build_direction_consistency_matrix( - # could we try to vectorize this? yes, lets compare both methods later tracklets: list[OCSORTTracklet], detection_boxes: np.ndarray, ) -> np.ndarray: + """Build direction consistency cost matrix (OCM) between tracklet velocities + and detection associations. + + This is the non-batch version kept for reference. Use + `build_direction_consistency_matrix_batch` for production. + + Args: + tracklets: List of OCSORTTracklet objects. + detection_boxes: Detection bounding boxes [x1, y1, x2, y2]. + + Returns: + np.ndarray: Direction consistency cost matrix (n_tracklets, n_detections). + """ n_tracklets = len(tracklets) n_detections = detection_boxes.shape[0] direction_consistency_matrix = np.zeros( @@ -36,9 +57,8 @@ def build_direction_consistency_matrix( ) for t, tracklet in enumerate(tracklets): - if tracklet.previous_to_last_observation is None: # if there is no previous box + if tracklet.previous_to_last_observation is None: continue - # In case the track is lost we use the last known box last_observation = tracklet.last_observation tracklet_speed = speed_direction( tracklet.previous_to_last_observation, last_observation @@ -48,23 +68,27 @@ def build_direction_consistency_matrix( detection_box = detection_boxes[d] association_speed = speed_direction(last_observation, detection_box) - # Compute cosine similarity cos_sim = np.dot(tracklet_speed, association_speed) cos_sim = np.clip(cos_sim, -1.0, 1.0) - # Apply the same transformation as original angle = np.arccos(cos_sim) direction_consistency_matrix[t, d] = (np.pi / 2.0 - np.abs(angle)) / np.pi - #direction_consistency_matrix[t, d] = cos_sim return direction_consistency_matrix -def speed_direction_batch(dets, tracks): # From oc sort repo - """Function for calculating direction between detections and tracks in a batch way. +def speed_direction_batch( + dets: np.ndarray, tracks: np.ndarray +) -> tuple[np.ndarray, np.ndarray]: + """Compute normalized direction vectors from tracks to detections in batch. Args: - dets: Detected bounding boxes in the form [x1, y1, x2, y2]. - tracks: Tracked bounding boxes in the form [x1, y1, x2, y2].""" + dets: Detection bounding boxes [x1, y1, x2, y2], shape (n_dets, 4). + tracks: Track bounding boxes [x1, y1, x2, y2], shape (n_tracks, 4). + + Returns: + tuple[np.ndarray, np.ndarray]: (dy, dx) direction vectors, + each of shape (n_tracks, n_dets). + """ tracks = tracks[..., np.newaxis] CX1, CY1 = (dets[:, 0] + dets[:, 2]) / 2.0, (dets[:, 1] + dets[:, 3]) / 2.0 CX2, CY2 = (tracks[:, 0] + tracks[:, 2]) / 2.0, (tracks[:, 1] + tracks[:, 3]) / 2.0 @@ -73,20 +97,31 @@ def speed_direction_batch(dets, tracks): # From oc sort repo norm = np.sqrt(dx**2 + dy**2) + 1e-6 dx = dx / norm dy = dy / norm - return dy, dx # size: num_track x num_det + return dy, dx def build_direction_consistency_matrix_batch( - # vectorized version from oc sort repo but adapted to our tracklet class tracklets: list[OCSORTTracklet], detection_boxes: np.ndarray, ) -> np.ndarray: + """Build direction consistency cost matrix (OCM) in batch - vectorized version. + + Computes similarity between tracklet velocity vectors and potential + association directions. Used in OC-SORT for motion-aware association. + + Args: + tracklets: List of OCSORTTracklet objects. + detection_boxes: Detection bounding boxes [x1, y1, x2, y2]. + + Returns: + np.ndarray: Direction consistency cost matrix (n_tracklets, n_detections). + """ n_tracklets = len(tracklets) n_detections = detection_boxes.shape[0] if len(detection_boxes) > 0 else 0 - + if n_tracklets == 0 or n_detections == 0: return np.zeros((n_tracklets, n_detections), dtype=np.float32) - + # Compute tracklet velocities (from previous_to_last -> last_observation) velocities = np.array( [ @@ -98,22 +133,24 @@ def build_direction_consistency_matrix_batch( for tracklet in tracklets ] ) # shape: (n_tracklets, 2) where each row is [dy, dx] - + # Get last observations as array for batch direction computation last_obs = np.array([tracklet.last_observation for tracklet in tracklets]) - + # Compute association directions (from last_observation -> detection) in batch # speed_direction_batch expects (dets, tracks) and returns (dy, dx) each of shape (n_tracks, n_dets) - Y, X = speed_direction_batch(detection_boxes, last_obs) # (n_tracklets, n_detections) - + Y, X = speed_direction_batch( + detection_boxes, last_obs + ) # (n_tracklets, n_detections) + # Expand velocities for broadcasting inertia_Y = velocities[:, 0:1] # (n_tracklets, 1) inertia_X = velocities[:, 1:2] # (n_tracklets, 1) - + # Compute cosine similarity (dot product of normalized vectors) diff_angle_cos = inertia_X * X + inertia_Y * Y # (n_tracklets, n_detections) diff_angle_cos = np.clip(diff_angle_cos, -1.0, 1.0) - + # Apply same transformation as non-batch version diff_angle = np.arccos(diff_angle_cos) angle_diff_cost = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi @@ -121,9 +158,9 @@ def build_direction_consistency_matrix_batch( # Mask out tracklets without previous observation valid_mask = np.array( [tracklet.previous_to_last_observation is not None for tracklet in tracklets], - dtype=np.float32 + dtype=np.float32, )[:, np.newaxis] # (n_tracklets, 1) - + angle_diff_cost = valid_mask * angle_diff_cost return angle_diff_cost.astype(np.float32) @@ -134,30 +171,28 @@ def add_track_id_detections( detection: sv.Detections, updated_detections: list[sv.Detections], minimum_consecutive_frames: int, - frame_count: int -) -> sv.Detections: - """ - The function prepares the updated Detections with track IDs. - If a tracker is "mature" (>= `minimum_consecutive_frames`) or recently updated, - it is assigned an ID to the detection that just updated it. + frame_count: int, +) -> None: + """Assign track ID to detection and add to updated_detections list. + + Handles ID assignment based on track maturity: + - Early frames (frame_count < minimum_consecutive_frames): Assign ID if + track was just updated and doesn't have an ID yet. + - Later frames: Assign ID only if track is mature (has enough consecutive + updates). Immature tracks get tracker_id = -1. Args: - detections: The latest set of object detections. - tracklets: List of OCSORTTracklet objects. - updated_detections: List of detections in which we add detections with assigned track IDs. - minimum_consecutive_frames: The number of consecutive frames required for a track to be considered "mature". - frame_count: The current frame count in the tracking process. - Returns: - sv.Detections: A copy of the detections with `tracker_id` set - for each detection that is tracked. - """ # noqa: E501 + track: The tracklet being processed. + detection: The detection to assign an ID to. + updated_detections: List to append the updated detection to. + minimum_consecutive_frames: Frames required for track maturity. + frame_count: Current frame number in tracking process. + """ new_det = deepcopy(detection) - # Add cast to clarify type for mypy - new_det = cast(sv.Detections, new_det) # ADDED cast + new_det = cast(sv.Detections, new_det) is_mature = ( - track.number_of_successful_consecutive_updates - >= minimum_consecutive_frames - ) + track.number_of_successful_consecutive_updates >= minimum_consecutive_frames + ) if frame_count < minimum_consecutive_frames: if track.time_since_update == 0: if track.tracker_id == -1: @@ -178,19 +213,17 @@ def add_track_id_detections( def get_iou_matrix( trackers: Sequence[OCSORTTracklet], detection_boxes: np.ndarray ) -> np.ndarray: - """ - Build IOU cost matrix between detections and predicted bounding boxes + """Build IOU cost matrix between trackers and detections. Args: - detection_boxes: Detected bounding boxes in the - form [x1, y1, x2, y2]. + trackers: Sequence of OCSORTTracklet objects. + detection_boxes: Detection bounding boxes [x1, y1, x2, y2]. Returns: - np.ndarray: IOU cost matrix. + np.ndarray: IOU matrix of shape (n_trackers, n_detections). """ predicted_boxes = np.array([t.get_state_bbox() for t in trackers]) if len(predicted_boxes) == 0 and len(trackers) > 0: - # Handle case where get_state_bbox might return empty array predicted_boxes = np.zeros((len(trackers), 4), dtype=np.float32) if len(trackers) > 0 and len(detection_boxes) > 0: @@ -204,20 +237,16 @@ def get_iou_matrix( def get_iou_matrix_between_boxes( last_observations: np.ndarray, detection_boxes: np.ndarray ) -> np.ndarray: - """ - Build IOU cost matrix between detections and predicted bounding boxes + """Build IOU cost matrix between two sets of bounding boxes. Args: - last_observations: Last observed bounding boxes of tracks in the - form [x1, y1, x2, y2]. - detection_boxes: Detected bounding boxes in the - form [x1, y1, x2, y2]. + last_observations: First set of boxes [x1, y1, x2, y2]. + detection_boxes: Second set of boxes [x1, y1, x2, y2]. Returns: - np.ndarray: IOU cost matrix. + np.ndarray: IOU matrix of shape (n_observations, n_detections). """ if len(last_observations) == 0 and len(last_observations) > 0: - # Handle case where get_state_bbox might return empty array last_observations = np.zeros((len(last_observations), 4), dtype=np.float32) if len(last_observations) > 0 and len(detection_boxes) > 0: From ac3258fd98f844fea173a51ccd90f38eae2c1c6b Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 6 Feb 2026 15:36:31 -0300 Subject: [PATCH 22/87] trying independence on original KF --- trackers/core/ocsort/tracker.py | 12 +- trackers/core/ocsort/tracklet.py | 243 +++++++++++++++-------------- trackers/utils/kalman_filter.py | 256 ++++++++++++++++--------------- 3 files changed, 267 insertions(+), 244 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 96bd055c..23057e47 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -68,6 +68,7 @@ def __init__( # Active tracks self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 + def _get_associated_indices( self, iou_matrix: np.ndarray, @@ -176,8 +177,11 @@ def update(self, detections: sv.Detections) -> sv.Detections: for row, col in matched_indices: self.tracks[row].update(detection_boxes[col]) add_track_id_detections( - self.tracks[row], detections[col : col + 1], updated_detections, - self.minimum_consecutive_frames, self.frame_count + self.tracks[row], + detections[col : col + 1], + updated_detections, + self.minimum_consecutive_frames, + self.frame_count, ) # Run 2nd Chance Association (OCR) @@ -219,9 +223,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: self._spawn_new_tracklets( detections[unmatched_detections], ocr_unmatched_detections ) - left_detections = detections[unmatched_detections][ - ocr_unmatched_detections - ] + left_detections = detections[unmatched_detections][ocr_unmatched_detections] left_detections.tracker_id = np.array( [-1] * len(left_detections), dtype=int ) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index ddbf0f9c..83c3baee 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -1,64 +1,46 @@ +from copy import deepcopy + import numpy as np -from trackers.core.ocsort.kalman_filter_ocsort import ( - KalmanFilterNew as KalmanFilterOCSORT, -) from trackers.utils.converters import ( xcycsr_to_xyxy, xyxy_to_xcycsr, ) +from trackers.utils.kalman_filter import KalmanFilter class OCSORTTracklet: - """ - The `OCSORTTracklet` class represents the internals of a single - tracked object (bounding box), with a Kalman filter to predict and update - its position. + """Tracklet for OC-SORT tracker with ORU (Observation-centric Re-Update). + + Manages a single tracked object with Kalman filter state estimation. + Implements OC-SORT specific features: + - Freeze/unfreeze for saving state before track is lost + - Virtual trajectory generation (ORU) for recovering lost tracks + Attributes: age: Age of the tracklet in frames. - kalman_filter: The Kalman filter instance for this tracklet. - tracker_id: Unique identifier for the tracker. - state_transition_matrix: State transition matrix for the Kalman filter. (referred as F) - number_of_successful_consecutive_updates: Number of times the object has been - updated successfully in a row. - time_since_update: Number of frames since the last update. - last_observation: The last observed bounding box. - previous_to_last_observation: The bounding box observed before the last one. - kalman_filter_state_before_being_lost: The Kalman filter state before the tracklet was lost. - kalman_filter_parameters_before_being_lost: The Kalman filter parameters before the tracklet was lost. - """ # noqa: E501 + kalman_filter: The Kalman filter instance for state estimation. + tracker_id: Unique identifier (-1 until track is mature). + number_of_successful_consecutive_updates: Consecutive successful updates. + time_since_update: Frames since last observation. + last_observation: Last observed bounding box [x1, y1, x2, y2]. + previous_to_last_observation: Second-to-last observation for velocity. + """ count_id: int = 0 - def __init__(self, initial_bbox) -> None: + def __init__(self, initial_bbox: np.ndarray) -> None: + """Initialize tracklet with first detection. + + Args: + initial_bbox: Initial bounding box [x1, y1, x2, y2]. + """ self.age = 0 - # state format: (x, y, s, r, vx, vy, vs). As detailed in SORT paper, r is the aspect ratio and constant! # noqa: E501 - # self.kalman_filter = KalmanFilter( - # bbox=xyxy_to_xcycsr(initial_bbox), - # state_dim=7, - # state_transition_matrix=np.array( - # [ - # [1, 0, 0, 0, 1, 0, 0], - # [0, 1, 0, 0, 0, 1, 0], - # [0, 0, 1, 0, 0, 0, 1], - # [0, 0, 0, 1, 0, 0, 0], - # [0, 0, 0, 0, 1, 0, 0], - # [0, 0, 0, 0, 0, 1, 0], - # [0, 0, 0, 0, 0, 0, 1], - # ] - # ), - # ) - - # self.kalman_filter.R[2:, 2:] *= 10.0 - # self.kalman_filter.P[4:, 4:] *= ( - # 1000.0 # give high uncertainty to the unobservable initial velocities - # ) - # self.kalman_filter.P *= 10.0 - # self.kalman_filter.Q[-1, -1] *= 0.01 - # self.kalman_filter.Q[4:, 4:] *= 0.01 - self.kalman_filter = KalmanFilterOCSORT(dim_x=7, dim_z=4) - - # State (x,y,s,r,vx,vy,vs): constant velocity model + + # Initialize Kalman filter: state (x, y, s, r, vx, vy, vs) + self.kalman_filter = KalmanFilter(dim_x=7, dim_z=4) + + # State transition: constant velocity model self.kalman_filter.F = np.array( [ [1, 0, 0, 0, 1, 0, 0], @@ -71,7 +53,8 @@ def __init__(self, initial_bbox) -> None: ], dtype=np.float64, ) - # Observe (x, y, s, r) from state + + # Measurement function: observe (x, y, s, r) from state self.kalman_filter.H = np.array( [ [1, 0, 0, 0, 0, 0, 0], @@ -82,116 +65,148 @@ def __init__(self, initial_bbox) -> None: dtype=np.float64, ) + # Noise tuning (from OC-SORT paper) self.kalman_filter.R[2:, 2:] *= 10.0 - self.kalman_filter.P[4:, 4:] *= ( - 1000.0 # give high uncertainty to the unobservable initial velocities - ) + self.kalman_filter.P[4:, 4:] *= 1000.0 # high uncertainty for velocities self.kalman_filter.P *= 10.0 self.kalman_filter.Q[-1, -1] *= 0.01 self.kalman_filter.Q[4:, 4:] *= 0.01 + # Initialize state with first observation self.kalman_filter.x[:4] = xyxy_to_xcycsr(initial_bbox).reshape((4, 1)) - self.last_observation = initial_bbox # None - self.previous_to_last_observation = None # For velocity of track - # Will be assigned a real ID when the track is considered mature + # Observation history for ORU + self.last_observation = initial_bbox + self.previous_to_last_observation: np.ndarray | None = None + + # Track ID (-1 until mature) self.tracker_id = -1 - # Number of hits indicates how many times the object has been - # updated successfully + # Tracking counters self.number_of_successful_consecutive_updates = 1 - # Number of frames since the last update self.time_since_update = 0 - # self.save_kalman_filter_state() - - # def save_kalman_filter_state(self) -> None: - # """Saves the current Kalman filter state and parameters.""" - # self.kalman_filter_state_before_being_lost = self.kalman_filter.state.copy() - # self.kalman_filter_parameters_before_being_lost = { - # "H": self.kalman_filter.H.copy(), - # "Q": self.kalman_filter.Q.copy(), - # "R": self.kalman_filter.R.copy(), - # "P": self.kalman_filter.P.copy(), - # } + + # ORU: saved state for freeze/unfreeze + self._frozen_state: dict | None = None + self._observed = True @classmethod def get_next_tracker_id(cls) -> int: + """Get next available tracker ID.""" next_id = cls.count_id cls.count_id += 1 return next_id - def update(self, bbox: np.ndarray) -> None: - """Updates the tracklet with a new bounding box observation. + def _freeze(self) -> None: + """Save Kalman filter state before track is lost (ORU mechanism).""" + self._frozen_state = self.kalman_filter.get_state() + + def _unfreeze(self, new_bbox: np.ndarray) -> None: + """Restore state and apply virtual trajectory (ORU mechanism). + + Generates linear interpolation between last observation and new + detection, then re-updates the Kalman filter through this virtual + trajectory. Args: - bbox (np.ndarray): The new bounding box in the form [x1, y1, x2, y2]. + new_bbox: New observation bounding box [x1, y1, x2, y2]. + """ + if self._frozen_state is None: + return + + # Restore to frozen state + self.kalman_filter.set_state(self._frozen_state) + + # Convert to (x, y, s, r) format + last_xcycsr = xyxy_to_xcycsr(self.last_observation) + new_xcycsr = xyxy_to_xcycsr(new_bbox) + + # Convert s, r back to w, h for interpolation + x1, y1, s1, r1 = last_xcycsr + w1 = np.sqrt(s1 * r1) + h1 = np.sqrt(s1 / r1) + + x2, y2, s2, r2 = new_xcycsr + w2 = np.sqrt(s2 * r2) + h2 = np.sqrt(s2 / r2) + + # Linear interpolation through missed frames + time_gap = self.time_since_update + dx = (x2 - x1) / time_gap + dy = (y2 - y1) / time_gap + dw = (w2 - w1) / time_gap + dh = (h2 - h1) / time_gap + + for i in range(1, time_gap + 1): + # Interpolate position and size + x = x1 + i * dx + y = y1 + i * dy + w = w1 + i * dw + h = h1 + i * dh + + # Convert back to (x, y, s, r) + s = w * h + r = w / h + virtual_obs = np.array([x, y, s, r]).reshape((4, 1)) + + # Re-update through virtual trajectory + if i < time_gap: + self.kalman_filter.predict() + self.kalman_filter.update(virtual_obs) + + self._frozen_state = None + + def update(self, bbox: np.ndarray | None) -> None: + """Update tracklet with new observation. + + Handles ORU: if track was lost and now observed again, + generates virtual trajectory to smooth the transition. + + Args: + bbox: Bounding box [x1, y1, x2, y2] or None for no observation. """ if bbox is not None: - self.kalman_filter.update(xyxy_to_xcycsr(bbox)) - # save the last before being lost KF parameters for re-updating + # Check if we need to unfreeze (was lost, now observed) + if not self._observed and self._frozen_state is not None: + self._unfreeze(bbox) + else: + self.kalman_filter.update(xyxy_to_xcycsr(bbox)) + + self._observed = True self.time_since_update = 0 self.number_of_successful_consecutive_updates += 1 self.previous_to_last_observation = self.last_observation self.last_observation = bbox else: + # No observation - freeze state if this is first miss + if self._observed: + self._freeze() + self._observed = False self.kalman_filter.update(None) def predict(self) -> np.ndarray: - """Predicts the next bounding box position using the Kalman filter. + """Predict next bounding box position. Returns: - np.ndarray: The predicted bounding box in the form [x1, y1, x2, y2]. + Predicted bounding box [x1, y1, x2, y2]. """ self.kalman_filter.predict() self.age += 1 + if self.time_since_update > 0: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 - return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4))) + return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4,))) - def is_lost( - self, - ) -> bool: - """Determines if the tracklet is considered lost.""" + def is_lost(self) -> bool: + """Check if tracklet is considered lost.""" return self.time_since_update > 1 - # def re_update(self, bbox: np.ndarray) -> None: - # """Re-updates the tracklet with the virtual trajectory generated out of the line that joins - # the last_observation and the parameter bbox. - - # Args: - # bbox: The new bounding box in the form [x1, y1, x2, y2]. - # """ - # self.kalman_filter.state = self.kalman_filter_state_before_being_lost - # self.kalman_filter.set_parameters( - # **self.kalman_filter_parameters_before_being_lost - # ) - # bbox_xywh = xyxy_to_xywh(np.array([bbox]))[0] - # last_observation_xywh = xyxy_to_xywh(np.array([self.last_observation]))[0] - # for i in range(1, self.time_since_update + 1): - # # Interpolate linearly between last_observation and bbox - # virtual_bbox_xywh = last_observation_xywh + ( - # bbox_xywh - last_observation_xywh - # ) * (i / (self.time_since_update)) - # virtual_bbox_xysa = np.copy(virtual_bbox_xywh) - # s = virtual_bbox_xywh[2] * virtual_bbox_xywh[3] # w*h - # virtual_bbox_xysa[2] = s - # virtual_bbox_xysa[3] = ( - # virtual_bbox_xywh[2] / virtual_bbox_xywh[3] - # ) # w/h = r - - # self.kalman_filter.predict() - # self.kalman_filter.update(virtual_bbox_xysa) - - # self.previous_to_last_observation = self.last_observation - # self.last_observation = bbox - # self.time_since_update = 0 - def get_state_bbox(self) -> np.ndarray: - """Returns the current bounding box estimate from the Kalman filter. + """Get current bounding box estimate from Kalman filter. Returns: - np.ndarray: The current bounding box in the form [x1, y1, x2, y2]. + Current bounding box estimate [x1, y1, x2, y2]. """ - return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4))) + return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4,))) diff --git a/trackers/utils/kalman_filter.py b/trackers/utils/kalman_filter.py index 792dfc93..7050efc7 100644 --- a/trackers/utils/kalman_filter.py +++ b/trackers/utils/kalman_filter.py @@ -1,150 +1,156 @@ +from copy import deepcopy + import numpy as np from numpy.typing import NDArray class KalmanFilter: - """ - The `OCSORTKalmanBoxTracker` class represents the internals of a single - tracked object (bounding box), with a Kalman filter to predict and update - its position. + """Generic Kalman filter implementation. + + A standard linear Kalman filter for state estimation. This is a clean, + general-purpose implementation that can be used by any tracker. Attributes: - tracker_id: Unique identifier for the tracker. - number_of_successful_updates: Number of times the object has been - updated successfully. - time_since_update: Number of frames since the last update. - state: State vector of the bounding box. - F: State transition matrix. - H: Measurement matrix. - Q: Process noise covariance matrix. - R: Measurement noise covariance matrix. - P: Error covariance matrix. - - Args: - bbox: Initial bounding box in the form [x1, y1, x2, y2]. - state_dim: Dimension of the state vector. Default is 7 using (x, y, s, r, vx, vy, vs). - state_transition_matrix: State transition matrix F. Default is identity. - """ # noqa: E501 - - state: NDArray[np.float32] - F: NDArray[np.float32] - H: NDArray[np.float32] - Q: NDArray[np.float32] - R: NDArray[np.float32] - P: NDArray[np.float32] - - def __init__( - self, - bbox: NDArray[np.float64], - state_dim: int = 7, - state_transition_matrix: np.ndarray = np.eye(7), - ) -> None: - # Number of hits indicates how many times the object has been - # updated successfully - self.number_of_successful_updates = 1 - # Number of frames since the last update - self.time_since_update = 0 - self.state_dim = state_dim - # For simplicity, we keep a small state vector: - # We'll store the bounding box in "self.state" - self.state = np.zeros((state_dim, 1), dtype=np.float32) - self.F = state_transition_matrix.astype(np.float32) - # Initialize state directly from the first detection - bbox_float: NDArray[np.float32] = bbox.astype(np.float32) - self.state[0, 0] = bbox_float[0] - self.state[1, 0] = bbox_float[1] - self.state[2, 0] = bbox_float[2] - self.state[3, 0] = bbox_float[3] - - # Basic constant velocity model - self._initialize_kalman_filter() - - def _initialize_kalman_filter(self) -> None: - """ - Sets up the matrices for the Kalman filter. + dim_x: Dimension of state vector. + dim_z: Dimension of measurement vector. + x: State vector (dim_x, 1). + P: State covariance matrix (dim_x, dim_x). + F: State transition matrix (dim_x, dim_x). + H: Measurement function matrix (dim_z, dim_x). + Q: Process noise covariance (dim_x, dim_x). + R: Measurement noise covariance (dim_z, dim_z). + x_prior: Prior state estimate (after predict, before update). + P_prior: Prior covariance (after predict, before update). + x_post: Posterior state estimate (after update). + P_post: Posterior covariance (after update). + + Example: + >>> kf = KalmanFilter(dim_x=4, dim_z=2) + >>> kf.x = np.array([[0], [0], [0], [0]]) # initial state + >>> kf.F = np.eye(4) # state transition + >>> kf.H = np.array([[1, 0, 0, 0], [0, 1, 0, 0]]) # measurement + >>> kf.predict() + >>> kf.update(np.array([[1], [2]])) + """ + + def __init__(self, dim_x: int, dim_z: int) -> None: + """Initialize Kalman filter. + + Args: + dim_x: Dimension of state vector. + dim_z: Dimension of measurement vector. """ - # We assume a constant velocity model. Positions are incremented by - # velocity each step. + if dim_x < 1: + raise ValueError("dim_x must be 1 or greater") + if dim_z < 1: + raise ValueError("dim_z must be 1 or greater") + + self.dim_x = dim_x + self.dim_z = dim_z + + # State and covariance + self.x: NDArray[np.float64] = np.zeros((dim_x, 1), dtype=np.float64) + self.P: NDArray[np.float64] = np.eye(dim_x, dtype=np.float64) + + # Process model + self.F: NDArray[np.float64] = np.eye(dim_x, dtype=np.float64) + self.Q: NDArray[np.float64] = np.eye(dim_x, dtype=np.float64) - # Measurement matrix (H) - self.H = np.eye(4, self.state_dim, dtype=np.float32) # 4xself.state_dim + # Measurement model + self.H: NDArray[np.float64] = np.zeros((dim_z, dim_x), dtype=np.float64) + self.R: NDArray[np.float64] = np.eye(dim_z, dtype=np.float64) - # Process covariance matrix (Q) - self.Q = np.eye(self.state_dim, dtype=np.float32) # * 0.01 + # Prior and posterior (for inspection/debugging) + self.x_prior: NDArray[np.float64] = self.x.copy() + self.P_prior: NDArray[np.float64] = self.P.copy() + self.x_post: NDArray[np.float64] = self.x.copy() + self.P_post: NDArray[np.float64] = self.P.copy() - # Measurement covariance (R): noise in detection - self.R = np.eye(4, dtype=np.float32) # * 0.1 + # Kalman gain, residual, system uncertainty (computed during update) + self.K: NDArray[np.float64] = np.zeros((dim_x, dim_z), dtype=np.float64) + self.y: NDArray[np.float64] = np.zeros((dim_z, 1), dtype=np.float64) + self.S: NDArray[np.float64] = np.zeros((dim_z, dim_z), dtype=np.float64) - # Error covariance matrix (P) - self.P = np.eye(self.state_dim, dtype=np.float32) + self._I: NDArray[np.float64] = np.eye(dim_x, dtype=np.float64) def predict(self) -> None: + """Predict next state (prior) using state transition model. + + Computes: + x = F @ x + P = F @ P @ F.T + Q """ - Predict the next state of the bounding box (applies the state transition). - """ - # Predict state - self.state = (self.F @ self.state).astype(np.float32) - # Predict error covariance - self.P = (self.F @ self.P @ self.F.T + self.Q).astype(np.float32) + self.x = self.F @ self.x + self.P = self.F @ self.P @ self.F.T + self.Q - # Increase time since update - self.time_since_update += 1 + # Save prior + self.x_prior = self.x.copy() + self.P_prior = self.P.copy() - def update(self, bbox: NDArray[np.float64]) -> None: - """ - Updates the state with a new detected bounding box. + def update(self, z: NDArray[np.float64] | None) -> None: + """Update state estimate with measurement. - Args: - bbox: Detected bounding box in the form [x1, y1, x2, y2]. - """ - # self.time_since_update = 0 - # self.number_of_successful_updates += 1 - - # Kalman Gain - S: NDArray[np.float32] = self.H @ self.P @ self.H.T + self.R - K: NDArray[np.float32] = (self.P @ self.H.T @ np.linalg.inv(S)).astype( - np.float32 - ) - - # Residual - measurement: NDArray[np.float32] = bbox.reshape((4, 1)).astype(np.float32) - y: NDArray[np.float32] = ( - measurement - self.H @ self.state - ) # y should be float32 (4,1) - - # Update state - self.state = (self.state + K @ y).astype(np.float32) - - # Update covariance - identity_matrix: NDArray[np.float32] = np.eye(self.state_dim, dtype=np.float32) - self.P = ((identity_matrix - K @ self.H) @ self.P).astype(np.float32) - - def set_parameters( - self, - H: NDArray[np.float32], - Q: NDArray[np.float32], - R: NDArray[np.float32], - P: NDArray[np.float32], - ) -> None: - """ - Sets the Kalman filter parameters. + If z is None, the state is not updated (prediction only). Args: - H: Measurement matrix. - Q: Process noise covariance matrix. - R: Measurement noise covariance matrix. - P: Error covariance matrix. + z: Measurement vector (dim_z, 1) or None for no observation. """ - self.H = H - self.Q = Q - self.R = R - self.P = P + if z is None: + # No observation - posterior equals prior + self.x_post = self.x.copy() + self.P_post = self.P.copy() + self.y = np.zeros((self.dim_z, 1), dtype=np.float64) + return - def get_state_bbox(self) -> NDArray[np.float32]: - """ - Returns the current bounding box estimate from the state vector. + # Ensure z is column vector + z = np.asarray(z, dtype=np.float64).reshape((self.dim_z, 1)) + + # Residual: y = z - H @ x + self.y = z - self.H @ self.x + + # System uncertainty: S = H @ P @ H.T + R + PHT = self.P @ self.H.T + self.S = self.H @ PHT + self.R + + # Kalman gain: K = P @ H.T @ S^-1 + self.K = PHT @ np.linalg.inv(self.S) + + # State update: x = x + K @ y + self.x = self.x + self.K @ self.y + + # Covariance update (Joseph form for numerical stability): + # P = (I - K @ H) @ P @ (I - K @ H).T + K @ R @ K.T + I_KH = self._I - self.K @ self.H + self.P = I_KH @ self.P @ I_KH.T + self.K @ self.R @ self.K.T + + # Save posterior + self.x_post = self.x.copy() + self.P_post = self.P.copy() + + def get_state(self) -> dict: + """Get current filter state for saving. Returns: - np.ndarray: The bounding box [x1, y1, x2, y2] + Dictionary with x, P, and other matrices. + """ + return { + "x": self.x.copy(), + "P": self.P.copy(), + "F": self.F.copy(), + "H": self.H.copy(), + "Q": self.Q.copy(), + "R": self.R.copy(), + } + + def set_state(self, state: dict) -> None: + """Restore filter state from saved dictionary. + + Args: + state: Dictionary from get_state(). """ - return self.state[:4, 0].flatten().astype(np.float32) + self.x = state["x"].copy() + self.P = state["P"].copy() + self.F = state["F"].copy() + self.H = state["H"].copy() + self.Q = state["Q"].copy() + self.R = state["R"].copy() From 2625c2da35b165fd37d40be22117b9b2368cbc4a Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Mon, 9 Feb 2026 10:04:36 -0300 Subject: [PATCH 23/87] fixed pre commit, deleted 2nd kalman filter --- trackers/core/ocsort/kalman_filter_ocsort.py | 1590 ------------------ trackers/core/ocsort/tracklet.py | 2 +- trackers/core/ocsort/utils.py | 1 - 3 files changed, 1 insertion(+), 1592 deletions(-) delete mode 100644 trackers/core/ocsort/kalman_filter_ocsort.py diff --git a/trackers/core/ocsort/kalman_filter_ocsort.py b/trackers/core/ocsort/kalman_filter_ocsort.py deleted file mode 100644 index 803e6da4..00000000 --- a/trackers/core/ocsort/kalman_filter_ocsort.py +++ /dev/null @@ -1,1590 +0,0 @@ -""" -This module implements the linear Kalman filter in both an object -oriented and procedural form. The KalmanFilter class implements -the filter by storing the various matrices in instance variables, -minimizing the amount of bookkeeping you have to do. -All Kalman filters operate with a predict->update cycle. The -predict step, implemented with the method or function predict(), -uses the state transition matrix F to predict the state in the next -time period (epoch). The state is stored as a gaussian (x, P), where -x is the state (column) vector, and P is its covariance. Covariance -matrix Q specifies the process covariance. In Bayesian terms, this -prediction is called the *prior*, which you can think of colloquially -as the estimate prior to incorporating the measurement. -The update step, implemented with the method or function `update()`, -incorporates the measurement z with covariance R, into the state -estimate (x, P). The class stores the system uncertainty in S, -the innovation (residual between prediction and measurement in -measurement space) in y, and the Kalman gain in k. The procedural -form returns these variables to you. In Bayesian terms this computes -the *posterior* - the estimate after the information from the -measurement is incorporated. -Whether you use the OO form or procedural form is up to you. If -matrices such as H, R, and F are changing each epoch, you'll probably -opt to use the procedural form. If they are unchanging, the OO -form is perhaps easier to use since you won't need to keep track -of these matrices. This is especially useful if you are implementing -banks of filters or comparing various KF designs for performance; -a trivial coding bug could lead to using the wrong sets of matrices. -This module also offers an implementation of the RTS smoother, and -other helper functions, such as log likelihood computations. -The Saver class allows you to easily save the state of the -KalmanFilter class after every update -This module expects NumPy arrays for all values that expect -arrays, although in a few cases, particularly method parameters, -it will accept types that convert to NumPy arrays, such as lists -of lists. These exceptions are documented in the method or function. -Examples --------- -The following example constructs a constant velocity kinematic -filter, filters noisy data, and plots the results. It also demonstrates -using the Saver class to save the state of the filter at each epoch. -.. code-block:: Python - import matplotlib.pyplot as plt - import numpy as np - from filterpy.kalman import KalmanFilter - from filterpy.common import Q_discrete_white_noise, Saver - r_std, q_std = 2., 0.003 - cv = KalmanFilter(dim_x=2, dim_z=1) - cv.x = np.array([[0., 1.]]) # position, velocity - cv.F = np.array([[1, dt],[ [0, 1]]) - cv.R = np.array([[r_std^^2]]) - f.H = np.array([[1., 0.]]) - f.P = np.diag([.1^^2, .03^^2) - f.Q = Q_discrete_white_noise(2, dt, q_std**2) - saver = Saver(cv) - for z in range(100): - cv.predict() - cv.update([z + randn() * r_std]) - saver.save() # save the filter's state - saver.to_array() - plt.plot(saver.x[:, 0]) - # plot all of the priors - plt.plot(saver.x_prior[:, 0]) - # plot mahalanobis distance - plt.figure() - plt.plot(saver.mahalanobis) -This code implements the same filter using the procedural form - x = np.array([[0., 1.]]) # position, velocity - F = np.array([[1, dt],[ [0, 1]]) - R = np.array([[r_std^^2]]) - H = np.array([[1., 0.]]) - P = np.diag([.1^^2, .03^^2) - Q = Q_discrete_white_noise(2, dt, q_std**2) - for z in range(100): - x, P = predict(x, P, F=F, Q=Q) - x, P = update(x, P, z=[z + randn() * r_std], R=R, H=H) - xs.append(x[0, 0]) - plt.plot(xs) -For more examples see the test subdirectory, or refer to the -book cited below. In it I both teach Kalman filtering from basic -principles, and teach the use of this library in great detail. -FilterPy library. -http://github.com/rlabbe/filterpy -Documentation at: -https://filterpy.readthedocs.org -Supporting book at: -https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python -This is licensed under an MIT license. See the readme.MD file -for more information. -Copyright 2014-2018 Roger R Labbe Jr. -""" - -from __future__ import absolute_import, division - -import sys -from copy import deepcopy -from math import exp, log, sqrt - -import numpy as np -import numpy.linalg as linalg -from filterpy.common import pretty_str, reshape_z -from filterpy.stats import logpdf -from numpy import dot, eye, isscalar, shape, zeros - - -class KalmanFilterNew(object): - """Implements a Kalman filter. You are responsible for setting the - various state variables to reasonable values; the defaults will - not give you a functional filter. - For now the best documentation is my free book Kalman and Bayesian - Filters in Python [2]_. The test files in this directory also give you a - basic idea of use, albeit without much description. - In brief, you will first construct this object, specifying the size of - the state vector with dim_x and the size of the measurement vector that - you will be using with dim_z. These are mostly used to perform size checks - when you assign values to the various matrices. For example, if you - specified dim_z=2 and then try to assign a 3x3 matrix to R (the - measurement noise matrix you will get an assert exception because R - should be 2x2. (If for whatever reason you need to alter the size of - things midstream just use the underscore version of the matrices to - assign directly: your_filter._R = a_3x3_matrix.) - After construction the filter will have default matrices created for you, - but you must specify the values for each. It’s usually easiest to just - overwrite them rather than assign to each element yourself. This will be - clearer in the example below. All are of type numpy.array. - Examples - -------- - Here is a filter that tracks position and velocity using a sensor that only - reads position. - First construct the object with the required dimensionality. Here the state - (`dim_x`) has 2 coefficients (position and velocity), and the measurement - (`dim_z`) has one. In FilterPy `x` is the state, `z` is the measurement. - .. code:: - from filterpy.kalman import KalmanFilter - f = KalmanFilter (dim_x=2, dim_z=1) - Assign the initial value for the state (position and velocity). You can do this - with a two dimensional array like so: - .. code:: - f.x = np.array([[2.], # position - [0.]]) # velocity - or just use a one dimensional array, which I prefer doing. - .. code:: - f.x = np.array([2., 0.]) - Define the state transition matrix: - .. code:: - f.F = np.array([[1.,1.], - [0.,1.]]) - Define the measurement function. Here we need to convert a position-velocity - vector into just a position vector, so we use: - .. code:: - f.H = np.array([[1., 0.]]) - Define the state's covariance matrix P. - .. code:: - f.P = np.array([[1000., 0.], - [ 0., 1000.] ]) - Now assign the measurement noise. Here the dimension is 1x1, so I can - use a scalar - .. code:: - f.R = 5 - I could have done this instead: - .. code:: - f.R = np.array([[5.]]) - Note that this must be a 2 dimensional array. - Finally, I will assign the process noise. Here I will take advantage of - another FilterPy library function: - .. code:: - from filterpy.common import Q_discrete_white_noise - f.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13) - Now just perform the standard predict/update loop: - .. code:: - while some_condition_is_true: - z = get_sensor_reading() - f.predict() - f.update(z) - do_something_with_estimate (f.x) - **Procedural Form** - This module also contains stand alone functions to perform Kalman filtering. - Use these if you are not a fan of objects. - **Example** - .. code:: - while True: - z, R = read_sensor() - x, P = predict(x, P, F, Q) - x, P = update(x, P, z, R, H) - See my book Kalman and Bayesian Filters in Python [2]_. - You will have to set the following attributes after constructing this - object for the filter to perform properly. Please note that there are - various checks in place to ensure that you have made everything the - 'correct' size. However, it is possible to provide incorrectly sized - arrays such that the linear algebra can not perform an operation. - It can also fail silently - you can end up with matrices of a size that - allows the linear algebra to work, but are the wrong shape for the problem - you are trying to solve. - Parameters - ---------- - dim_x : int - Number of state variables for the Kalman filter. For example, if - you are tracking the position and velocity of an object in two - dimensions, dim_x would be 4. - This is used to set the default size of P, Q, and u - dim_z : int - Number of of measurement inputs. For example, if the sensor - provides you with position in (x,y), dim_z would be 2. - dim_u : int (optional) - size of the control input, if it is being used. - Default value of 0 indicates it is not used. - compute_log_likelihood : bool (default = True) - Computes log likelihood by default, but this can be a slow - computation, so if you never use it you can turn this computation - off. - Attributes - ---------- - x : numpy.array(dim_x, 1) - Current state estimate. Any call to update() or predict() updates - this variable. - P : numpy.array(dim_x, dim_x) - Current state covariance matrix. Any call to update() or predict() - updates this variable. - x_prior : numpy.array(dim_x, 1) - Prior (predicted) state estimate. The *_prior and *_post attributes - are for convenience; they store the prior and posterior of the - current epoch. Read Only. - P_prior : numpy.array(dim_x, dim_x) - Prior (predicted) state covariance matrix. Read Only. - x_post : numpy.array(dim_x, 1) - Posterior (updated) state estimate. Read Only. - P_post : numpy.array(dim_x, dim_x) - Posterior (updated) state covariance matrix. Read Only. - z : numpy.array - Last measurement used in update(). Read only. - R : numpy.array(dim_z, dim_z) - Measurement noise covariance matrix. Also known as the - observation covariance. - Q : numpy.array(dim_x, dim_x) - Process noise covariance matrix. Also known as the transition - covariance. - F : numpy.array() - State Transition matrix. Also known as `A` in some formulation. - H : numpy.array(dim_z, dim_x) - Measurement function. Also known as the observation matrix, or as `C`. - y : numpy.array - Residual of the update step. Read only. - K : numpy.array(dim_x, dim_z) - Kalman gain of the update step. Read only. - S : numpy.array - System uncertainty (P projected to measurement space). Read only. - SI : numpy.array - Inverse system uncertainty. Read only. - log_likelihood : float - log-likelihood of the last measurement. Read only. - likelihood : float - likelihood of last measurement. Read only. - Computed from the log-likelihood. The log-likelihood can be very - small, meaning a large negative value such as -28000. Taking the - exp() of that results in 0.0, which can break typical algorithms - which multiply by this value, so by default we always return a - number >= sys.float_info.min. - mahalanobis : float - mahalanobis distance of the innovation. Read only. - inv : function, default numpy.linalg.inv - If you prefer another inverse function, such as the Moore-Penrose - pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv - This is only used to invert self.S. If you know it is diagonal, you - might choose to set it to filterpy.common.inv_diagonal, which is - several times faster than numpy.linalg.inv for diagonal matrices. - alpha : float - Fading memory setting. 1.0 gives the normal Kalman filter, and - values slightly larger than 1.0 (such as 1.02) give a fading - memory effect - previous measurements have less influence on the - filter's estimates. This formulation of the Fading memory filter - (there are many) is due to Dan Simon [1]_. - References - ---------- - .. [1] Dan Simon. "Optimal State Estimation." John Wiley & Sons. - p. 208-212. (2006) - .. [2] Roger Labbe. "Kalman and Bayesian Filters in Python" - https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python - """ - - def __init__(self, dim_x, dim_z, dim_u=0): - if dim_x < 1: - raise ValueError("dim_x must be 1 or greater") - if dim_z < 1: - raise ValueError("dim_z must be 1 or greater") - if dim_u < 0: - raise ValueError("dim_u must be 0 or greater") - - self.dim_x = dim_x - self.dim_z = dim_z - self.dim_u = dim_u - - self.x = zeros((dim_x, 1)) # state - self.P = eye(dim_x) # uncertainty covariance - self.Q = eye(dim_x) # process uncertainty - self.B = None # control transition matrix - self.F = eye(dim_x) # state transition matrix - self.H = zeros((dim_z, dim_x)) # measurement function - self.R = eye(dim_z) # measurement uncertainty - self._alpha_sq = 1.0 # fading memory control - self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation - self.z = np.array([[None] * self.dim_z]).T - - # gain and residual are computed during the innovation step. We - # save them so that in case you want to inspect them for various - # purposes - self.K = np.zeros((dim_x, dim_z)) # kalman gain - self.y = zeros((dim_z, 1)) - self.S = np.zeros((dim_z, dim_z)) # system uncertainty - self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty - - # identity matrix. Do not alter this. - self._I = np.eye(dim_x) - - # these will always be a copy of x,P after predict() is called - self.x_prior = self.x.copy() - self.P_prior = self.P.copy() - - # these will always be a copy of x,P after update() is called - self.x_post = self.x.copy() - self.P_post = self.P.copy() - - # Only computed only if requested via property - self._log_likelihood = log(sys.float_info.min) - self._likelihood = sys.float_info.min - self._mahalanobis = None - - # keep all observations - self.history_obs = [] - - self.inv = np.linalg.inv - - self.attr_saved = None - self.observed = False - - def predict(self, u=None, B=None, F=None, Q=None): - """ - Predict next state (prior) using the Kalman filter state propagation - equations. - Parameters - ---------- - u : np.array, default 0 - Optional control vector. - B : np.array(dim_x, dim_u), or None - Optional control transition matrix; a value of None - will cause the filter to use `self.B`. - F : np.array(dim_x, dim_x), or None - Optional state transition matrix; a value of None - will cause the filter to use `self.F`. - Q : np.array(dim_x, dim_x), scalar, or None - Optional process noise matrix; a value of None will cause the - filter to use `self.Q`. - """ - - if B is None: - B = self.B - if F is None: - F = self.F - if Q is None: - Q = self.Q - elif isscalar(Q): - Q = eye(self.dim_x) * Q - - # x = Fx + Bu - if B is not None and u is not None: - self.x = dot(F, self.x) + dot(B, u) - else: - self.x = dot(F, self.x) - - # P = FPF' + Q - self.P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q - - # save prior - self.x_prior = self.x.copy() - self.P_prior = self.P.copy() - - def freeze(self): - """ - Save the parameters before non-observation forward - """ - self.attr_saved = deepcopy(self.__dict__) - - def unfreeze(self): - if self.attr_saved is not None: - new_history = deepcopy(self.history_obs) - self.__dict__ = self.attr_saved - # self.history_obs = new_history - self.history_obs = self.history_obs[:-1] - occur = [int(d is None) for d in new_history] - indices = np.where(np.array(occur) == 0)[0] - index1 = indices[-2] - index2 = indices[-1] - box1 = new_history[index1] - x1, y1, s1, r1 = box1 - w1 = np.sqrt(s1 * r1) - h1 = np.sqrt(s1 / r1) - box2 = new_history[index2] - x2, y2, s2, r2 = box2 - w2 = np.sqrt(s2 * r2) - h2 = np.sqrt(s2 / r2) - time_gap = index2 - index1 - dx = (x2 - x1) / time_gap - dy = (y2 - y1) / time_gap - dw = (w2 - w1) / time_gap - dh = (h2 - h1) / time_gap - for i in range(index2 - index1): - """ - The default virtual trajectory generation is by linear - motion (constant speed hypothesis), you could modify this - part to implement your own. - """ - x = x1 + (i + 1) * dx - y = y1 + (i + 1) * dy - w = w1 + (i + 1) * dw - h = h1 + (i + 1) * dh - s = w * h - r = w / float(h) - new_box = np.array([x, y, s, r]).reshape((4, 1)) - """ - I still use predict-update loop here to refresh the parameters, - but this can be faster by directly modifying the internal parameters - as suggested in the paper. I keep this naive but slow way for - easy read and understanding - """ - self.update(new_box) - if not i == (index2 - index1 - 1): - self.predict() - - def update(self, z, R=None, H=None): - """ - Add a new measurement (z) to the Kalman filter. - If z is None, nothing is computed. However, x_post and P_post are - updated with the prior (x_prior, P_prior), and self.z is set to None. - Parameters - ---------- - z : (dim_z, 1): array_like - measurement for this update. z can be a scalar if dim_z is 1, - otherwise it must be convertible to a column vector. - If you pass in a value of H, z must be a column vector the - of the correct size. - R : np.array, scalar, or None - Optionally provide R to override the measurement noise for this - one call, otherwise self.R will be used. - H : np.array, or None - Optionally provide H to override the measurement function for this - one call, otherwise self.H will be used. - """ - - # set to None to force recompute - self._log_likelihood = None - self._likelihood = None - self._mahalanobis = None - - # append the observation - self.history_obs.append(z) - - if z is None: - if self.observed: - """ - Got no observation so freeze the current parameters for future - potential online smoothing. - """ - self.freeze() - self.observed = False - self.z = np.array([[None] * self.dim_z]).T - self.x_post = self.x.copy() - self.P_post = self.P.copy() - self.y = zeros((self.dim_z, 1)) - return - - # self.observed = True - if not self.observed: - """ - Get observation, use online smoothing to re-update parameters - """ - self.unfreeze() - self.observed = True - - if R is None: - R = self.R - elif isscalar(R): - R = eye(self.dim_z) * R - - if H is None: - z = reshape_z(z, self.dim_z, self.x.ndim) - H = self.H - - # y = z - Hx - # error (residual) between measurement and prediction - self.y = z - dot(H, self.x) - - # common subexpression for speed - PHT = dot(self.P, H.T) - - # S = HPH' + R - # project system uncertainty into measurement space - self.S = dot(H, PHT) + R - self.SI = self.inv(self.S) - # K = PH'inv(S) - # map system uncertainty into kalman gain - self.K = dot(PHT, self.SI) - - # x = x + Ky - # predict new x with residual scaled by the kalman gain - self.x = self.x + dot(self.K, self.y) - - # P = (I-KH)P(I-KH)' + KRK' - # This is more numerically stable - # and works for non-optimal K vs the equation - # P = (I-KH)P usually seen in the literature. - - I_KH = self._I - dot(self.K, H) - self.P = dot(dot(I_KH, self.P), I_KH.T) + dot(dot(self.K, R), self.K.T) - - # save measurement and posterior state - self.z = deepcopy(z) - self.x_post = self.x.copy() - self.P_post = self.P.copy() - - def predict_steadystate(self, u=0, B=None): - """ - Predict state (prior) using the Kalman filter state propagation - equations. Only x is updated, P is left unchanged. See - update_steadstate() for a longer explanation of when to use this - method. - Parameters - ---------- - u : np.array - Optional control vector. If non-zero, it is multiplied by B - to create the control input into the system. - B : np.array(dim_x, dim_u), or None - Optional control transition matrix; a value of None - will cause the filter to use `self.B`. - """ - - if B is None: - B = self.B - - # x = Fx + Bu - if B is not None: - self.x = dot(self.F, self.x) + dot(B, u) - else: - self.x = dot(self.F, self.x) - - # save prior - self.x_prior = self.x.copy() - self.P_prior = self.P.copy() - - def update_steadystate(self, z): - """ - Add a new measurement (z) to the Kalman filter without recomputing - the Kalman gain K, the state covariance P, or the system - uncertainty S. - You can use this for LTI systems since the Kalman gain and covariance - converge to a fixed value. Precompute these and assign them explicitly, - or run the Kalman filter using the normal predict()/update(0 cycle - until they converge. - The main advantage of this call is speed. We do significantly less - computation, notably avoiding a costly matrix inversion. - Use in conjunction with predict_steadystate(), otherwise P will grow - without bound. - Parameters - ---------- - z : (dim_z, 1): array_like - measurement for this update. z can be a scalar if dim_z is 1, - otherwise it must be convertible to a column vector. - Examples - -------- - >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter - >>> # let filter converge on representative data, then save k and P - >>> for i in range(100): - >>> cv.predict() - >>> cv.update([i, i, i]) - >>> saved_k = np.copy(cv.K) - >>> saved_P = np.copy(cv.P) - later on: - >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter - >>> cv.K = np.copy(saved_K) - >>> cv.P = np.copy(saved_P) - >>> for i in range(100): - >>> cv.predict_steadystate() - >>> cv.update_steadystate([i, i, i]) - """ - - # set to None to force recompute - self._log_likelihood = None - self._likelihood = None - self._mahalanobis = None - - if z is None: - self.z = np.array([[None] * self.dim_z]).T - self.x_post = self.x.copy() - self.P_post = self.P.copy() - self.y = zeros((self.dim_z, 1)) - return - - z = reshape_z(z, self.dim_z, self.x.ndim) - - # y = z - Hx - # error (residual) between measurement and prediction - self.y = z - dot(self.H, self.x) - - # x = x + Ky - # predict new x with residual scaled by the kalman gain - self.x = self.x + dot(self.K, self.y) - - self.z = deepcopy(z) - self.x_post = self.x.copy() - self.P_post = self.P.copy() - - # set to None to force recompute - self._log_likelihood = None - self._likelihood = None - self._mahalanobis = None - - def update_correlated(self, z, R=None, H=None): - """Add a new measurement (z) to the Kalman filter assuming that - process noise and measurement noise are correlated as defined in - the `self.M` matrix. - A partial derivation can be found in [1] - If z is None, nothing is changed. - Parameters - ---------- - z : (dim_z, 1): array_like - measurement for this update. z can be a scalar if dim_z is 1, - otherwise it must be convertible to a column vector. - R : np.array, scalar, or None - Optionally provide R to override the measurement noise for this - one call, otherwise self.R will be used. - H : np.array, or None - Optionally provide H to override the measurement function for this - one call, otherwise self.H will be used. - References - ---------- - .. [1] Bulut, Y. (2011). Applied Kalman filter theory (Doctoral dissertation, Northeastern University). - http://people.duke.edu/~hpgavin/SystemID/References/Balut-KalmanFilter-PhD-NEU-2011.pdf - """ - - # set to None to force recompute - self._log_likelihood = None - self._likelihood = None - self._mahalanobis = None - - if z is None: - self.z = np.array([[None] * self.dim_z]).T - self.x_post = self.x.copy() - self.P_post = self.P.copy() - self.y = zeros((self.dim_z, 1)) - return - - if R is None: - R = self.R - elif isscalar(R): - R = eye(self.dim_z) * R - - # rename for readability and a tiny extra bit of speed - if H is None: - z = reshape_z(z, self.dim_z, self.x.ndim) - H = self.H - - # handle special case: if z is in form [[z]] but x is not a column - # vector dimensions will not match - if self.x.ndim == 1 and shape(z) == (1, 1): - z = z[0] - - if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3) - z = np.asarray([z]) - - # y = z - Hx - # error (residual) between measurement and prediction - self.y = z - dot(H, self.x) - - # common subexpression for speed - PHT = dot(self.P, H.T) - - # project system uncertainty into measurement space - self.S = dot(H, PHT) + dot(H, self.M) + dot(self.M.T, H.T) + R - self.SI = self.inv(self.S) - - # K = PH'inv(S) - # map system uncertainty into kalman gain - self.K = dot(PHT + self.M, self.SI) - - # x = x + Ky - # predict new x with residual scaled by the kalman gain - self.x = self.x + dot(self.K, self.y) - self.P = self.P - dot(self.K, dot(H, self.P) + self.M.T) - - self.z = deepcopy(z) - self.x_post = self.x.copy() - self.P_post = self.P.copy() - - def batch_filter( - self, - zs, - Fs=None, - Qs=None, - Hs=None, - Rs=None, - Bs=None, - us=None, - update_first=False, - saver=None, - ): - """Batch processes a sequences of measurements. - Parameters - ---------- - zs : list-like - list of measurements at each time step `self.dt`. Missing - measurements must be represented by `None`. - Fs : None, list-like, default=None - optional value or list of values to use for the state transition - matrix F. - If Fs is None then self.F is used for all epochs. - Otherwise it must contain a list-like list of F's, one for - each epoch. This allows you to have varying F per epoch. - Qs : None, np.array or list-like, default=None - optional value or list of values to use for the process error - covariance Q. - If Qs is None then self.Q is used for all epochs. - Otherwise it must contain a list-like list of Q's, one for - each epoch. This allows you to have varying Q per epoch. - Hs : None, np.array or list-like, default=None - optional list of values to use for the measurement matrix H. - If Hs is None then self.H is used for all epochs. - If Hs contains a single matrix, then it is used as H for all - epochs. - Otherwise it must contain a list-like list of H's, one for - each epoch. This allows you to have varying H per epoch. - Rs : None, np.array or list-like, default=None - optional list of values to use for the measurement error - covariance R. - If Rs is None then self.R is used for all epochs. - Otherwise it must contain a list-like list of R's, one for - each epoch. This allows you to have varying R per epoch. - Bs : None, np.array or list-like, default=None - optional list of values to use for the control transition matrix B. - If Bs is None then self.B is used for all epochs. - Otherwise it must contain a list-like list of B's, one for - each epoch. This allows you to have varying B per epoch. - us : None, np.array or list-like, default=None - optional list of values to use for the control input vector; - If us is None then None is used for all epochs (equivalent to 0, - or no control input). - Otherwise it must contain a list-like list of u's, one for - each epoch. - update_first : bool, optional, default=False - controls whether the order of operations is update followed by - predict, or predict followed by update. Default is predict->update. - saver : filterpy.common.Saver, optional - filterpy.common.Saver object. If provided, saver.save() will be - called after every epoch - Returns - ------- - means : np.array((n,dim_x,1)) - array of the state for each time step after the update. Each entry - is an np.array. In other words `means[k,:]` is the state at step - `k`. - covariance : np.array((n,dim_x,dim_x)) - array of the covariances for each time step after the update. - In other words `covariance[k,:,:]` is the covariance at step `k`. - means_predictions : np.array((n,dim_x,1)) - array of the state for each time step after the predictions. Each - entry is an np.array. In other words `means[k,:]` is the state at - step `k`. - covariance_predictions : np.array((n,dim_x,dim_x)) - array of the covariances for each time step after the prediction. - In other words `covariance[k,:,:]` is the covariance at step `k`. - Examples - -------- - .. code-block:: Python - # this example demonstrates tracking a measurement where the time - # between measurement varies, as stored in dts. This requires - # that F be recomputed for each epoch. The output is then smoothed - # with an RTS smoother. - zs = [t + random.randn()*4 for t in range (40)] - Fs = [np.array([[1., dt], [0, 1]] for dt in dts] - (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs) - (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs) - """ - - # pylint: disable=too-many-statements - n = np.size(zs, 0) - if Fs is None: - Fs = [self.F] * n - if Qs is None: - Qs = [self.Q] * n - if Hs is None: - Hs = [self.H] * n - if Rs is None: - Rs = [self.R] * n - if Bs is None: - Bs = [self.B] * n - if us is None: - us = [0] * n - - # mean estimates from Kalman Filter - if self.x.ndim == 1: - means = zeros((n, self.dim_x)) - means_p = zeros((n, self.dim_x)) - else: - means = zeros((n, self.dim_x, 1)) - means_p = zeros((n, self.dim_x, 1)) - - # state covariances from Kalman Filter - covariances = zeros((n, self.dim_x, self.dim_x)) - covariances_p = zeros((n, self.dim_x, self.dim_x)) - - if update_first: - for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): - self.update(z, R=R, H=H) - means[i, :] = self.x - covariances[i, :, :] = self.P - - self.predict(u=u, B=B, F=F, Q=Q) - means_p[i, :] = self.x - covariances_p[i, :, :] = self.P - - if saver is not None: - saver.save() - else: - for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): - self.predict(u=u, B=B, F=F, Q=Q) - means_p[i, :] = self.x - covariances_p[i, :, :] = self.P - - self.update(z, R=R, H=H) - means[i, :] = self.x - covariances[i, :, :] = self.P - - if saver is not None: - saver.save() - - return (means, covariances, means_p, covariances_p) - - def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv): - """ - Runs the Rauch-Tung-Striebel Kalman smoother on a set of - means and covariances computed by a Kalman filter. The usual input - would come from the output of `KalmanFilter.batch_filter()`. - Parameters - ---------- - Xs : numpy.array - array of the means (state variable x) of the output of a Kalman - filter. - Ps : numpy.array - array of the covariances of the output of a kalman filter. - Fs : list-like collection of numpy.array, optional - State transition matrix of the Kalman filter at each time step. - Optional, if not provided the filter's self.F will be used - Qs : list-like collection of numpy.array, optional - Process noise of the Kalman filter at each time step. Optional, - if not provided the filter's self.Q will be used - inv : function, default numpy.linalg.inv - If you prefer another inverse function, such as the Moore-Penrose - pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv - Returns - ------- - x : numpy.ndarray - smoothed means - P : numpy.ndarray - smoothed state covariances - K : numpy.ndarray - smoother gain at each step - Pp : numpy.ndarray - Predicted state covariances - Examples - -------- - .. code-block:: Python - zs = [t + random.randn()*4 for t in range (40)] - (mu, cov, _, _) = kalman.batch_filter(zs) - (x, P, K, Pp) = rts_smoother(mu, cov, kf.F, kf.Q) - """ - - if len(Xs) != len(Ps): - raise ValueError("length of Xs and Ps must be the same") - - n = Xs.shape[0] - dim_x = Xs.shape[1] - - if Fs is None: - Fs = [self.F] * n - if Qs is None: - Qs = [self.Q] * n - - # smoother gain - K = zeros((n, dim_x, dim_x)) - - x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy() - for k in range(n - 2, -1, -1): - Pp[k] = dot(dot(Fs[k + 1], P[k]), Fs[k + 1].T) + Qs[k + 1] - - # pylint: disable=bad-whitespace - K[k] = dot(dot(P[k], Fs[k + 1].T), inv(Pp[k])) - x[k] += dot(K[k], x[k + 1] - dot(Fs[k + 1], x[k])) - P[k] += dot(dot(K[k], P[k + 1] - Pp[k]), K[k].T) - - return (x, P, K, Pp) - - def get_prediction(self, u=None, B=None, F=None, Q=None): - """ - Predict next state (prior) using the Kalman filter state propagation - equations and returns it without modifying the object. - Parameters - ---------- - u : np.array, default 0 - Optional control vector. - B : np.array(dim_x, dim_u), or None - Optional control transition matrix; a value of None - will cause the filter to use `self.B`. - F : np.array(dim_x, dim_x), or None - Optional state transition matrix; a value of None - will cause the filter to use `self.F`. - Q : np.array(dim_x, dim_x), scalar, or None - Optional process noise matrix; a value of None will cause the - filter to use `self.Q`. - Returns - ------- - (x, P) : tuple - State vector and covariance array of the prediction. - """ - - if B is None: - B = self.B - if F is None: - F = self.F - if Q is None: - Q = self.Q - elif isscalar(Q): - Q = eye(self.dim_x) * Q - - # x = Fx + Bu - if B is not None and u is not None: - x = dot(F, self.x) + dot(B, u) - else: - x = dot(F, self.x) - - # P = FPF' + Q - P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q - - return x, P - - def get_update(self, z=None): - """ - Computes the new estimate based on measurement `z` and returns it - without altering the state of the filter. - Parameters - ---------- - z : (dim_z, 1): array_like - measurement for this update. z can be a scalar if dim_z is 1, - otherwise it must be convertible to a column vector. - Returns - ------- - (x, P) : tuple - State vector and covariance array of the update. - """ - - if z is None: - return self.x, self.P - z = reshape_z(z, self.dim_z, self.x.ndim) - - R = self.R - H = self.H - P = self.P - x = self.x - - # error (residual) between measurement and prediction - y = z - dot(H, x) - - # common subexpression for speed - PHT = dot(P, H.T) - - # project system uncertainty into measurement space - S = dot(H, PHT) + R - - # map system uncertainty into kalman gain - K = dot(PHT, self.inv(S)) - - # predict new x with residual scaled by the kalman gain - x = x + dot(K, y) - - # P = (I-KH)P(I-KH)' + KRK' - I_KH = self._I - dot(K, H) - P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T) - - return x, P - - def residual_of(self, z): - """ - Returns the residual for the given measurement (z). Does not alter - the state of the filter. - """ - z = reshape_z(z, self.dim_z, self.x.ndim) - return z - dot(self.H, self.x_prior) - - def measurement_of_state(self, x): - """ - Helper function that converts a state into a measurement. - Parameters - ---------- - x : np.array - kalman state vector - Returns - ------- - z : (dim_z, 1): array_like - measurement for this update. z can be a scalar if dim_z is 1, - otherwise it must be convertible to a column vector. - """ - - return dot(self.H, x) - - @property - def log_likelihood(self): - """ - log-likelihood of the last measurement. - """ - if self._log_likelihood is None: - self._log_likelihood = logpdf(x=self.y, cov=self.S) - return self._log_likelihood - - @property - def likelihood(self): - """ - Computed from the log-likelihood. The log-likelihood can be very - small, meaning a large negative value such as -28000. Taking the - exp() of that results in 0.0, which can break typical algorithms - which multiply by this value, so by default we always return a - number >= sys.float_info.min. - """ - if self._likelihood is None: - self._likelihood = exp(self.log_likelihood) - if self._likelihood == 0: - self._likelihood = sys.float_info.min - return self._likelihood - - @property - def mahalanobis(self): - """ " - Mahalanobis distance of measurement. E.g. 3 means measurement - was 3 standard deviations away from the predicted value. - Returns - ------- - mahalanobis : float - """ - if self._mahalanobis is None: - self._mahalanobis = sqrt(float(dot(dot(self.y.T, self.SI), self.y))) - return self._mahalanobis - - @property - def alpha(self): - """ - Fading memory setting. 1.0 gives the normal Kalman filter, and - values slightly larger than 1.0 (such as 1.02) give a fading - memory effect - previous measurements have less influence on the - filter's estimates. This formulation of the Fading memory filter - (there are many) is due to Dan Simon [1]_. - """ - return self._alpha_sq**0.5 - - def log_likelihood_of(self, z): - """ - log likelihood of the measurement `z`. This should only be called - after a call to update(). Calling after predict() will yield an - incorrect result.""" - - if z is None: - return log(sys.float_info.min) - return logpdf(z, dot(self.H, self.x), self.S) - - @alpha.setter - def alpha(self, value): - if not np.isscalar(value) or value < 1: - raise ValueError("alpha must be a float greater than 1") - - self._alpha_sq = value**2 - - def __repr__(self): - return "\n".join( - [ - "KalmanFilter object", - pretty_str("dim_x", self.dim_x), - pretty_str("dim_z", self.dim_z), - pretty_str("dim_u", self.dim_u), - pretty_str("x", self.x), - pretty_str("P", self.P), - pretty_str("x_prior", self.x_prior), - pretty_str("P_prior", self.P_prior), - pretty_str("x_post", self.x_post), - pretty_str("P_post", self.P_post), - pretty_str("F", self.F), - pretty_str("Q", self.Q), - pretty_str("R", self.R), - pretty_str("H", self.H), - pretty_str("K", self.K), - pretty_str("y", self.y), - pretty_str("S", self.S), - pretty_str("SI", self.SI), - pretty_str("M", self.M), - pretty_str("B", self.B), - pretty_str("z", self.z), - pretty_str("log-likelihood", self.log_likelihood), - pretty_str("likelihood", self.likelihood), - pretty_str("mahalanobis", self.mahalanobis), - pretty_str("alpha", self.alpha), - pretty_str("inv", self.inv), - ] - ) - - def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None): - """ - Performs a series of asserts to check that the size of everything - is what it should be. This can help you debug problems in your design. - If you pass in H, R, F, Q those will be used instead of this object's - value for those matrices. - Testing `z` (the measurement) is problamatic. x is a vector, and can be - implemented as either a 1D array or as a nx1 column vector. Thus Hx - can be of different shapes. Then, if Hx is a single value, it can - be either a 1D array or 2D vector. If either is true, z can reasonably - be a scalar (either '3' or np.array('3') are scalars under this - definition), a 1D, 1 element array, or a 2D, 1 element array. You are - allowed to pass in any combination that works. - """ - - if H is None: - H = self.H - if R is None: - R = self.R - if F is None: - F = self.F - if Q is None: - Q = self.Q - x = self.x - P = self.P - - assert x.ndim == 1 or x.ndim == 2, ( - "x must have one or two dimensions, but has {}".format(x.ndim) - ) - - if x.ndim == 1: - assert x.shape[0] == self.dim_x, ( - "Shape of x must be ({},{}), but is {}".format(self.dim_x, 1, x.shape) - ) - else: - assert x.shape == (self.dim_x, 1), ( - "Shape of x must be ({},{}), but is {}".format(self.dim_x, 1, x.shape) - ) - - assert P.shape == (self.dim_x, self.dim_x), ( - "Shape of P must be ({},{}), but is {}".format( - self.dim_x, self.dim_x, P.shape - ) - ) - - assert Q.shape == (self.dim_x, self.dim_x), ( - "Shape of Q must be ({},{}), but is {}".format( - self.dim_x, self.dim_x, P.shape - ) - ) - - assert F.shape == (self.dim_x, self.dim_x), ( - "Shape of F must be ({},{}), but is {}".format( - self.dim_x, self.dim_x, F.shape - ) - ) - - assert np.ndim(H) == 2, "Shape of H must be (dim_z, {}), but is {}".format( - P.shape[0], shape(H) - ) - - assert H.shape[1] == P.shape[0], ( - "Shape of H must be (dim_z, {}), but is {}".format(P.shape[0], H.shape) - ) - - # shape of R must be the same as HPH' - hph_shape = (H.shape[0], H.shape[0]) - r_shape = shape(R) - - if H.shape[0] == 1: - # r can be scalar, 1D, or 2D in this case - assert r_shape in [(), (1,), (1, 1)], ( - "R must be scalar or one element array, but is shaped {}".format( - r_shape - ) - ) - else: - assert r_shape == hph_shape, "shape of R should be {} but it is {}".format( - hph_shape, r_shape - ) - - if z is not None: - z_shape = shape(z) - else: - z_shape = (self.dim_z, 1) - - # H@x must have shape of z - Hx = dot(H, x) - - if z_shape == (): # scalar or np.array(scalar) - assert Hx.ndim == 1 or shape(Hx) == (1, 1), ( - "shape of z should be {}, not {} for the given H".format( - shape(Hx), z_shape - ) - ) - - elif shape(Hx) == (1,): - assert z_shape[0] == 1, "Shape of z must be {} for the given H".format( - shape(Hx) - ) - - else: - assert z_shape == shape(Hx) or ( - len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1) - ), "shape of z should be {}, not {} for the given H".format( - shape(Hx), z_shape - ) - - if np.ndim(Hx) > 1 and shape(Hx) != (1, 1): - assert shape(Hx) == z_shape, ( - "shape of z should be {} for the given H, but it is {}".format( - shape(Hx), z_shape - ) - ) - - -def update(x, P, z, R, H=None, return_all=False): - """ - Add a new measurement (z) to the Kalman filter. If z is None, nothing - is changed. - This can handle either the multidimensional or unidimensional case. If - all parameters are floats instead of arrays the filter will still work, - and return floats for x, P as the result. - update(1, 2, 1, 1, 1) # univariate - update(x, P, 1 - Parameters - ---------- - x : numpy.array(dim_x, 1), or float - State estimate vector - P : numpy.array(dim_x, dim_x), or float - Covariance matrix - z : (dim_z, 1): array_like - measurement for this update. z can be a scalar if dim_z is 1, - otherwise it must be convertible to a column vector. - R : numpy.array(dim_z, dim_z), or float - Measurement noise matrix - H : numpy.array(dim_x, dim_x), or float, optional - Measurement function. If not provided, a value of 1 is assumed. - return_all : bool, default False - If true, y, K, S, and log_likelihood are returned, otherwise - only x and P are returned. - Returns - ------- - x : numpy.array - Posterior state estimate vector - P : numpy.array - Posterior covariance matrix - y : numpy.array or scalar - Residua. Difference between measurement and state in measurement space - K : numpy.array - Kalman gain - S : numpy.array - System uncertainty in measurement space - log_likelihood : float - log likelihood of the measurement - """ - - # pylint: disable=bare-except - - if z is None: - if return_all: - return x, P, None, None, None, None - return x, P - - if H is None: - H = np.array([1]) - - if np.isscalar(H): - H = np.array([H]) - - Hx = np.atleast_1d(dot(H, x)) - z = reshape_z(z, Hx.shape[0], x.ndim) - - # error (residual) between measurement and prediction - y = z - Hx - - # project system uncertainty into measurement space - S = dot(dot(H, P), H.T) + R - - # map system uncertainty into kalman gain - try: - K = dot(dot(P, H.T), linalg.inv(S)) - except: - # can't invert a 1D array, annoyingly - K = dot(dot(P, H.T), 1.0 / S) - - # predict new x with residual scaled by the kalman gain - x = x + dot(K, y) - - # P = (I-KH)P(I-KH)' + KRK' - KH = dot(K, H) - - try: - I_KH = np.eye(KH.shape[0]) - KH - except: - I_KH = np.array([1 - KH]) - P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T) - - if return_all: - # compute log likelihood - log_likelihood = logpdf(z, dot(H, x), S) - return x, P, y, K, S, log_likelihood - return x, P - - -def update_steadystate(x, z, K, H=None): - """ - Add a new measurement (z) to the Kalman filter. If z is None, nothing - is changed. - Parameters - ---------- - x : numpy.array(dim_x, 1), or float - State estimate vector - z : (dim_z, 1): array_like - measurement for this update. z can be a scalar if dim_z is 1, - otherwise it must be convertible to a column vector. - K : numpy.array, or float - Kalman gain matrix - H : numpy.array(dim_x, dim_x), or float, optional - Measurement function. If not provided, a value of 1 is assumed. - Returns - ------- - x : numpy.array - Posterior state estimate vector - Examples - -------- - This can handle either the multidimensional or unidimensional case. If - all parameters are floats instead of arrays the filter will still work, - and return floats for x, P as the result. - >>> update_steadystate(1, 2, 1) # univariate - >>> update_steadystate(x, P, z, H) - """ - - if z is None: - return x - - if H is None: - H = np.array([1]) - - if np.isscalar(H): - H = np.array([H]) - - Hx = np.atleast_1d(dot(H, x)) - z = reshape_z(z, Hx.shape[0], x.ndim) - - # error (residual) between measurement and prediction - y = z - Hx - - # estimate new x with residual scaled by the kalman gain - return x + dot(K, y) - - -def predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.0): - """ - Predict next state (prior) using the Kalman filter state propagation - equations. - Parameters - ---------- - x : numpy.array - State estimate vector - P : numpy.array - Covariance matrix - F : numpy.array() - State Transition matrix - Q : numpy.array, Optional - Process noise matrix - u : numpy.array, Optional, default 0. - Control vector. If non-zero, it is multiplied by B - to create the control input into the system. - B : numpy.array, optional, default 0. - Control transition matrix. - alpha : float, Optional, default=1.0 - Fading memory setting. 1.0 gives the normal Kalman filter, and - values slightly larger than 1.0 (such as 1.02) give a fading - memory effect - previous measurements have less influence on the - filter's estimates. This formulation of the Fading memory filter - (there are many) is due to Dan Simon - Returns - ------- - x : numpy.array - Prior state estimate vector - P : numpy.array - Prior covariance matrix - """ - - if np.isscalar(F): - F = np.array(F) - x = dot(F, x) + dot(B, u) - P = (alpha * alpha) * dot(dot(F, P), F.T) + Q - - return x, P - - -def predict_steadystate(x, F=1, u=0, B=1): - """ - Predict next state (prior) using the Kalman filter state propagation - equations. This steady state form only computes x, assuming that the - covariance is constant. - Parameters - ---------- - x : numpy.array - State estimate vector - P : numpy.array - Covariance matrix - F : numpy.array() - State Transition matrix - u : numpy.array, Optional, default 0. - Control vector. If non-zero, it is multiplied by B - to create the control input into the system. - B : numpy.array, optional, default 0. - Control transition matrix. - Returns - ------- - x : numpy.array - Prior state estimate vector - """ - - if np.isscalar(F): - F = np.array(F) - x = dot(F, x) + dot(B, u) - - return x - - -def batch_filter( - x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None, update_first=False, saver=None -): - """ - Batch processes a sequences of measurements. - Parameters - ---------- - zs : list-like - list of measurements at each time step. Missing measurements must be - represented by None. - Fs : list-like - list of values to use for the state transition matrix matrix. - Qs : list-like - list of values to use for the process error - covariance. - Hs : list-like - list of values to use for the measurement matrix. - Rs : list-like - list of values to use for the measurement error - covariance. - Bs : list-like, optional - list of values to use for the control transition matrix; - a value of None in any position will cause the filter - to use `self.B` for that time step. - us : list-like, optional - list of values to use for the control input vector; - a value of None in any position will cause the filter to use - 0 for that time step. - update_first : bool, optional - controls whether the order of operations is update followed by - predict, or predict followed by update. Default is predict->update. - saver : filterpy.common.Saver, optional - filterpy.common.Saver object. If provided, saver.save() will be - called after every epoch - Returns - ------- - means : np.array((n,dim_x,1)) - array of the state for each time step after the update. Each entry - is an np.array. In other words `means[k,:]` is the state at step - `k`. - covariance : np.array((n,dim_x,dim_x)) - array of the covariances for each time step after the update. - In other words `covariance[k,:,:]` is the covariance at step `k`. - means_predictions : np.array((n,dim_x,1)) - array of the state for each time step after the predictions. Each - entry is an np.array. In other words `means[k,:]` is the state at - step `k`. - covariance_predictions : np.array((n,dim_x,dim_x)) - array of the covariances for each time step after the prediction. - In other words `covariance[k,:,:]` is the covariance at step `k`. - Examples - -------- - .. code-block:: Python - zs = [t + random.randn()*4 for t in range (40)] - Fs = [kf.F for t in range (40)] - Hs = [kf.H for t in range (40)] - (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None, - Bs=None, us=None, update_first=False) - (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None) - """ - - n = np.size(zs, 0) - dim_x = x.shape[0] - - # mean estimates from Kalman Filter - if x.ndim == 1: - means = zeros((n, dim_x)) - means_p = zeros((n, dim_x)) - else: - means = zeros((n, dim_x, 1)) - means_p = zeros((n, dim_x, 1)) - - # state covariances from Kalman Filter - covariances = zeros((n, dim_x, dim_x)) - covariances_p = zeros((n, dim_x, dim_x)) - - if us is None: - us = [0.0] * n - Bs = [0.0] * n - - if update_first: - for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): - x, P = update(x, P, z, R=R, H=H) - means[i, :] = x - covariances[i, :, :] = P - - x, P = predict(x, P, u=u, B=B, F=F, Q=Q) - means_p[i, :] = x - covariances_p[i, :, :] = P - if saver is not None: - saver.save() - else: - for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)): - x, P = predict(x, P, u=u, B=B, F=F, Q=Q) - means_p[i, :] = x - covariances_p[i, :, :] = P - - x, P = update(x, P, z, R=R, H=H) - means[i, :] = x - covariances[i, :, :] = P - if saver is not None: - saver.save() - - return (means, covariances, means_p, covariances_p) - - -def rts_smoother(Xs, Ps, Fs, Qs): - """ - Runs the Rauch-Tung-Striebel Kalman smoother on a set of - means and covariances computed by a Kalman filter. The usual input - would come from the output of `KalmanFilter.batch_filter()`. - Parameters - ---------- - Xs : numpy.array - array of the means (state variable x) of the output of a Kalman - filter. - Ps : numpy.array - array of the covariances of the output of a kalman filter. - Fs : list-like collection of numpy.array - State transition matrix of the Kalman filter at each time step. - Qs : list-like collection of numpy.array, optional - Process noise of the Kalman filter at each time step. - Returns - ------- - x : numpy.ndarray - smoothed means - P : numpy.ndarray - smoothed state covariances - K : numpy.ndarray - smoother gain at each step - pP : numpy.ndarray - predicted state covariances - Examples - -------- - .. code-block:: Python - zs = [t + random.randn()*4 for t in range (40)] - (mu, cov, _, _) = kalman.batch_filter(zs) - (x, P, K, pP) = rts_smoother(mu, cov, kf.F, kf.Q) - """ - - if len(Xs) != len(Ps): - raise ValueError("length of Xs and Ps must be the same") - - n = Xs.shape[0] - dim_x = Xs.shape[1] - - # smoother gain - K = zeros((n, dim_x, dim_x)) - x, P, pP = Xs.copy(), Ps.copy(), Ps.copy() - - for k in range(n - 2, -1, -1): - pP[k] = dot(dot(Fs[k], P[k]), Fs[k].T) + Qs[k] - - # pylint: disable=bad-whitespace - K[k] = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k])) - x[k] += dot(K[k], x[k + 1] - dot(Fs[k], x[k])) - P[k] += dot(dot(K[k], P[k + 1] - pP[k]), K[k].T) - - return (x, P, K, pP) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 83c3baee..abd45546 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -1,4 +1,4 @@ -from copy import deepcopy +from __future__ import annotations import numpy as np diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 5fe7d49d..7986b0e8 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -138,7 +138,6 @@ def build_direction_consistency_matrix_batch( last_obs = np.array([tracklet.last_observation for tracklet in tracklets]) # Compute association directions (from last_observation -> detection) in batch - # speed_direction_batch expects (dets, tracks) and returns (dy, dx) each of shape (n_tracks, n_dets) Y, X = speed_direction_batch( detection_boxes, last_obs ) # (n_tracklets, n_detections) From 475c07b4ce64609e0c849462bbe398e11bf7af7f Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Mon, 9 Feb 2026 10:18:11 -0300 Subject: [PATCH 24/87] added XYXY and XCTCSR possible reps --- trackers/core/ocsort/tracklet.py | 166 +++++++++++++++++++++++++------ 1 file changed, 135 insertions(+), 31 deletions(-) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index abd45546..69e705a7 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -1,5 +1,7 @@ from __future__ import annotations +from enum import Enum + import numpy as np from trackers.utils.converters import ( @@ -9,6 +11,22 @@ from trackers.utils.kalman_filter import KalmanFilter +class StateRepresentation(Enum): + """Kalman filter state representation for bounding boxes. + + XCYCSR: Center-based (x_center, y_center, scale, aspect_ratio, vx, vy, vs) + - 7 state variables, aspect ratio is constant (no velocity) + - Used in original SORT/OC-SORT papers + + XYXY: Corner-based (x1, y1, x2, y2, vx1, vy1, vx2, vy2) + - 8 state variables, all coordinates have velocities + - More direct representation, potentially better for non-rigid objects + """ + + XCYCSR = "xcycsr" + XYXY = "xyxy" + + class OCSORTTracklet: """Tracklet for OC-SORT tracker with ORU (Observation-centric Re-Update). @@ -16,6 +34,7 @@ class OCSORTTracklet: Implements OC-SORT specific features: - Freeze/unfreeze for saving state before track is lost - Virtual trajectory generation (ORU) for recovering lost tracks + - Configurable state representation (XCYCSR or XYXY) Attributes: age: Age of the tracklet in frames. @@ -25,45 +44,72 @@ class OCSORTTracklet: time_since_update: Frames since last observation. last_observation: Last observed bounding box [x1, y1, x2, y2]. previous_to_last_observation: Second-to-last observation for velocity. + state_repr: The state representation being used. """ count_id: int = 0 - def __init__(self, initial_bbox: np.ndarray) -> None: + def __init__( + self, + initial_bbox: np.ndarray, + state_repr: StateRepresentation = StateRepresentation.XYXY, + ) -> None: """Initialize tracklet with first detection. Args: initial_bbox: Initial bounding box [x1, y1, x2, y2]. + state_repr: State representation to use (XCYCSR or XYXY). """ self.age = 0 + self.state_repr = state_repr + + # Initialize Kalman filter based on state representation + if state_repr == StateRepresentation.XCYCSR: + self._init_xcycsr_filter(initial_bbox) + else: + self._init_xyxy_filter(initial_bbox) + + # Observation history for ORU + self.last_observation = initial_bbox + self.previous_to_last_observation: np.ndarray | None = None + + # Track ID (-1 until mature) + self.tracker_id = -1 + + # Tracking counters + self.number_of_successful_consecutive_updates = 1 + self.time_since_update = 0 - # Initialize Kalman filter: state (x, y, s, r, vx, vy, vs) + # ORU: saved state for freeze/unfreeze + self._frozen_state: dict | None = None + self._observed = True + + def _init_xcycsr_filter(self, initial_bbox: np.ndarray) -> None: + """Initialize Kalman filter with XCYCSR state representation. + + State: [x_center, y_center, scale, aspect_ratio, vx, vy, vs] + Measurement: [x_center, y_center, scale, aspect_ratio] + """ self.kalman_filter = KalmanFilter(dim_x=7, dim_z=4) # State transition: constant velocity model + # fmt: off self.kalman_filter.F = np.array( [ [1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], - [0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0], # aspect ratio: no velocity [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1], ], dtype=np.float64, ) + # fmt: on # Measurement function: observe (x, y, s, r) from state - self.kalman_filter.H = np.array( - [ - [1, 0, 0, 0, 0, 0, 0], - [0, 1, 0, 0, 0, 0, 0], - [0, 0, 1, 0, 0, 0, 0], - [0, 0, 0, 1, 0, 0, 0], - ], - dtype=np.float64, - ) + self.kalman_filter.H = np.eye(4, 7, dtype=np.float64) # Noise tuning (from OC-SORT paper) self.kalman_filter.R[2:, 2:] *= 10.0 @@ -75,20 +121,56 @@ def __init__(self, initial_bbox: np.ndarray) -> None: # Initialize state with first observation self.kalman_filter.x[:4] = xyxy_to_xcycsr(initial_bbox).reshape((4, 1)) - # Observation history for ORU - self.last_observation = initial_bbox - self.previous_to_last_observation: np.ndarray | None = None + def _init_xyxy_filter(self, initial_bbox: np.ndarray) -> None: + """Initialize Kalman filter with XYXY state representation. - # Track ID (-1 until mature) - self.tracker_id = -1 + State: [x1, y1, x2, y2, vx1, vy1, vx2, vy2] + Measurement: [x1, y1, x2, y2] + """ + self.kalman_filter = KalmanFilter(dim_x=8, dim_z=4) - # Tracking counters - self.number_of_successful_consecutive_updates = 1 - self.time_since_update = 0 + # State transition: constant velocity model for all coordinates + # fmt: off + self.kalman_filter.F = np.array( + [ + [1, 0, 0, 0, 1, 0, 0, 0], # x1 += vx1 + [0, 1, 0, 0, 0, 1, 0, 0], # y1 += vy1 + [0, 0, 1, 0, 0, 0, 1, 0], # x2 += vx2 + [0, 0, 0, 1, 0, 0, 0, 1], # y2 += vy2 + [0, 0, 0, 0, 1, 0, 0, 0], # vx1 + [0, 0, 0, 0, 0, 1, 0, 0], # vy1 + [0, 0, 0, 0, 0, 0, 1, 0], # vx2 + [0, 0, 0, 0, 0, 0, 0, 1], # vy2 + ], + dtype=np.float64, + ) + # fmt: on - # ORU: saved state for freeze/unfreeze - self._frozen_state: dict | None = None - self._observed = True + # Measurement function: observe (x1, y1, x2, y2) from state + self.kalman_filter.H = np.eye(4, 8, dtype=np.float64) + + # Noise tuning (similar scaling to XCYCSR version) + self.kalman_filter.R *= 1.0 # measurement noise + self.kalman_filter.P[4:, 4:] *= 1000.0 # high uncertainty for velocities + self.kalman_filter.P *= 10.0 + self.kalman_filter.Q[4:, 4:] *= 0.01 + + # Initialize state with first observation (direct XYXY) + self.kalman_filter.x[:4] = initial_bbox.reshape((4, 1)) + + def _bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: + """Convert bbox to measurement based on state representation.""" + if self.state_repr == StateRepresentation.XCYCSR: + return xyxy_to_xcycsr(bbox) + else: + return bbox + + def _state_to_bbox(self) -> np.ndarray: + """Convert current state to xyxy bbox.""" + if self.state_repr == StateRepresentation.XCYCSR: + return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4,))) + else: + return self.kalman_filter.x[:4].reshape((4,)) @classmethod def get_next_tracker_id(cls) -> int: @@ -117,6 +199,17 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: # Restore to frozen state self.kalman_filter.set_state(self._frozen_state) + time_gap = self.time_since_update + + if self.state_repr == StateRepresentation.XCYCSR: + self._unfreeze_xcycsr(new_bbox, time_gap) + else: + self._unfreeze_xyxy(new_bbox, time_gap) + + self._frozen_state = None + + def _unfreeze_xcycsr(self, new_bbox: np.ndarray, time_gap: int) -> None: + """ORU interpolation for XCYCSR representation.""" # Convert to (x, y, s, r) format last_xcycsr = xyxy_to_xcycsr(self.last_observation) new_xcycsr = xyxy_to_xcycsr(new_bbox) @@ -130,15 +223,13 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: w2 = np.sqrt(s2 * r2) h2 = np.sqrt(s2 / r2) - # Linear interpolation through missed frames - time_gap = self.time_since_update + # Linear interpolation deltas dx = (x2 - x1) / time_gap dy = (y2 - y1) / time_gap dw = (w2 - w1) / time_gap dh = (h2 - h1) / time_gap for i in range(1, time_gap + 1): - # Interpolate position and size x = x1 + i * dx y = y1 + i * dy w = w1 + i * dw @@ -149,12 +240,24 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: r = w / h virtual_obs = np.array([x, y, s, r]).reshape((4, 1)) - # Re-update through virtual trajectory if i < time_gap: self.kalman_filter.predict() self.kalman_filter.update(virtual_obs) - self._frozen_state = None + def _unfreeze_xyxy(self, new_bbox: np.ndarray, time_gap: int) -> None: + """ORU interpolation for XYXY representation.""" + last_xyxy = self.last_observation + new_xyxy = new_bbox + + # Linear interpolation deltas for each coordinate + delta = (new_xyxy - last_xyxy) / time_gap + + for i in range(1, time_gap + 1): + virtual_obs = (last_xyxy + i * delta).reshape((4, 1)) + + if i < time_gap: + self.kalman_filter.predict() + self.kalman_filter.update(virtual_obs) def update(self, bbox: np.ndarray | None) -> None: """Update tracklet with new observation. @@ -170,7 +273,8 @@ def update(self, bbox: np.ndarray | None) -> None: if not self._observed and self._frozen_state is not None: self._unfreeze(bbox) else: - self.kalman_filter.update(xyxy_to_xcycsr(bbox)) + measurement = self._bbox_to_measurement(bbox) + self.kalman_filter.update(measurement) self._observed = True self.time_since_update = 0 @@ -197,7 +301,7 @@ def predict(self) -> np.ndarray: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 - return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4,))) + return self._state_to_bbox() def is_lost(self) -> bool: """Check if tracklet is considered lost.""" @@ -209,4 +313,4 @@ def get_state_bbox(self) -> np.ndarray: Returns: Current bounding box estimate [x1, y1, x2, y2]. """ - return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4,))) + return self._state_to_bbox() From 146907dc19cfa967e2ac668dde1b79f4bd6d35b2 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Mon, 9 Feb 2026 11:12:25 -0300 Subject: [PATCH 25/87] code looking good --- trackers/core/ocsort/tracker.py | 10 ++++------ trackers/core/ocsort/tracklet.py | 9 +++------ trackers/core/ocsort/utils.py | 9 ++++----- 3 files changed, 11 insertions(+), 17 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 23057e47..8385bd39 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -18,7 +18,7 @@ class OCSORTTracker(BaseTracker): OC-SORT remains Simple, Online, and Real-Time but improves robustness during occlusion and non-linear motion. It recognizes limitations from SORT and the linear motion assumption of the Kalman filter, and adds three mechanisms to enhance tracking. The first mechanism is Observation-Centre Re-Update (ORU), which runs a - predict-update loop with a 'virtual trajectory' depending on the last observation and new observation when + predict-update loop with a 'virtual trajectory' in order to have less noisy Kalman Filter parameters once a track is re-activated after being lost. The second mechanism is Observation-Centric Momentum (OCM), that incorporates the direction consistency of tracks in the cost matrix for the association. Finally, OC-SORT adds Observation-centric Recovery (OCR), a second-stage association step between the last observation of unmatched @@ -52,7 +52,7 @@ def __init__( self, lost_track_buffer: int = 30, frame_rate: float = 30.0, - minimum_consecutive_frames: int = 3, # should change this for min_hits? + minimum_consecutive_frames: int = 3, minimum_iou_threshold: float = 0.3, direction_consistency_weight: float = 0.2, high_conf_det_threshold: float = 0.6, @@ -65,7 +65,7 @@ def __init__( self.minimum_iou_threshold = minimum_iou_threshold self.direction_consistency_weight = direction_consistency_weight self.high_conf_det_threshold = high_conf_det_threshold - # Active tracks + self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 @@ -214,8 +214,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: self.frame_count, ) - # Update OCR-unmatched tracks with None before filtering; indices - # refer to current self.tracks. + # Update OCR-unmatched tracks with None before filtering (marks as lost for re-update) #noqa: E501 for m in _ocr_unmatched_tracks: self.tracks[unmatched_tracks[m]].update(None) @@ -267,7 +266,6 @@ def activate_or_kill_tracklets(self): tracklet.number_of_successful_consecutive_updates >= self.minimum_consecutive_frames ) - # is_active = tracklet.time_since_update == 0 if tracklet.time_since_update <= self.maximum_frames_without_update: alive_tracklets.append(tracklet) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 69e705a7..31084fb4 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -52,7 +52,7 @@ class OCSORTTracklet: def __init__( self, initial_bbox: np.ndarray, - state_repr: StateRepresentation = StateRepresentation.XYXY, + state_repr: StateRepresentation = StateRepresentation.XCYCSR, ) -> None: """Initialize tracklet with first detection. @@ -73,7 +73,8 @@ def __init__( self.last_observation = initial_bbox self.previous_to_last_observation: np.ndarray | None = None - # Track ID (-1 until mature) + # Track ID can be initialized before mature in oc-sort + # it is assigned if the frame number is less than minimum_consecutive_frames self.tracker_id = -1 # Tracking counters @@ -93,7 +94,6 @@ def _init_xcycsr_filter(self, initial_bbox: np.ndarray) -> None: self.kalman_filter = KalmanFilter(dim_x=7, dim_z=4) # State transition: constant velocity model - # fmt: off self.kalman_filter.F = np.array( [ [1, 0, 0, 0, 1, 0, 0], @@ -106,7 +106,6 @@ def _init_xcycsr_filter(self, initial_bbox: np.ndarray) -> None: ], dtype=np.float64, ) - # fmt: on # Measurement function: observe (x, y, s, r) from state self.kalman_filter.H = np.eye(4, 7, dtype=np.float64) @@ -130,7 +129,6 @@ def _init_xyxy_filter(self, initial_bbox: np.ndarray) -> None: self.kalman_filter = KalmanFilter(dim_x=8, dim_z=4) # State transition: constant velocity model for all coordinates - # fmt: off self.kalman_filter.F = np.array( [ [1, 0, 0, 0, 1, 0, 0, 0], # x1 += vx1 @@ -144,7 +142,6 @@ def _init_xyxy_filter(self, initial_bbox: np.ndarray) -> None: ], dtype=np.float64, ) - # fmt: on # Measurement function: observe (x1, y1, x2, y2) from state self.kalman_filter.H = np.eye(4, 8, dtype=np.float64) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 7986b0e8..29eb412d 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -40,8 +40,8 @@ def build_direction_consistency_matrix( """Build direction consistency cost matrix (OCM) between tracklet velocities and detection associations. - This is the non-batch version kept for reference. Use - `build_direction_consistency_matrix_batch` for production. + Note: This is the non-batch version kept for reference, interpretability and testing + purposes. Use `build_direction_consistency_matrix_batch` for production. Args: tracklets: List of OCSORTTracklet objects. @@ -132,7 +132,7 @@ def build_direction_consistency_matrix_batch( else np.array([0.0, 0.0]) for tracklet in tracklets ] - ) # shape: (n_tracklets, 2) where each row is [dy, dx] + ) # Get last observations as array for batch direction computation last_obs = np.array([tracklet.last_observation for tracklet in tracklets]) @@ -147,10 +147,9 @@ def build_direction_consistency_matrix_batch( inertia_X = velocities[:, 1:2] # (n_tracklets, 1) # Compute cosine similarity (dot product of normalized vectors) - diff_angle_cos = inertia_X * X + inertia_Y * Y # (n_tracklets, n_detections) + diff_angle_cos = inertia_X * X + inertia_Y * Y diff_angle_cos = np.clip(diff_angle_cos, -1.0, 1.0) - # Apply same transformation as non-batch version diff_angle = np.arccos(diff_angle_cos) angle_diff_cost = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi From e94b2076645fba6f52b8d75d05a08de90ffdf26f Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Mon, 9 Feb 2026 13:02:26 -0300 Subject: [PATCH 26/87] added metrics --- README.md | 2 +- docs/trackers/ocsort.md | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6ef840fd..849bc736 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ Trackers gives you clean, modular re-implementations of leading multi-object tra |:-------------:|:-------------------------------------------------------------------------------:|:----------:|:-----------:|:----------:|:--------------:|:--------------:| | SORT | [`SORTTracker`](https://trackers.roboflow.com/develop/trackers/sort/) | 58.4 | 69.9 | 67.2 | 70.9 | 81.6 | | ByteTrack | [`ByteTrackTracker`](https://trackers.roboflow.com/develop/trackers/bytetrack/) | **60.1** | **73.2** | **74.1** | **73.0** | **84.0** | -| OC-SORT | `OCSORTTracker` | — | — | — | — | — | +| OC-SORT | `OCSORTTracker` | — | — | — | 71.5 | 78.6 | ## Quickstart diff --git a/docs/trackers/ocsort.md b/docs/trackers/ocsort.md index 478c21ae..ba9dbec9 100644 --- a/docs/trackers/ocsort.md +++ b/docs/trackers/ocsort.md @@ -17,9 +17,9 @@ For comparisons with other trackers, plus full details on the datasets and evalu | Dataset | HOTA | IDF1 | MOTA | |:---------:|:----:|:----:|:----:| -| MOT17 | 59.1 | 71.8 | 76.6 | -| SportsMOT | 69.9 | 69.2 | 95.0 | -| SoccerNet | 77.8 | 72.1 | 93.9 | +| MOT17 | ---- | ---- | ---- | +| SportsMOT | 71.5 | 71.2 | 95.2 | +| SoccerNet | 78.6 | 72.7 | 94.5 | ## Run on video, webcam, or RTSP stream From 517b1e1fa4b5e69cf1c4630a83c6e38fdca99a1c Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 10 Feb 2026 10:47:02 -0300 Subject: [PATCH 27/87] trying interpolation in xyxy system --- trackers/core/ocsort/tracklet.py | 41 ++++++++++---------------------- 1 file changed, 13 insertions(+), 28 deletions(-) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 31084fb4..8e2a80a8 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -206,36 +206,21 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: self._frozen_state = None def _unfreeze_xcycsr(self, new_bbox: np.ndarray, time_gap: int) -> None: - """ORU interpolation for XCYCSR representation.""" - # Convert to (x, y, s, r) format - last_xcycsr = xyxy_to_xcycsr(self.last_observation) - new_xcycsr = xyxy_to_xcycsr(new_bbox) - - # Convert s, r back to w, h for interpolation - x1, y1, s1, r1 = last_xcycsr - w1 = np.sqrt(s1 * r1) - h1 = np.sqrt(s1 / r1) - - x2, y2, s2, r2 = new_xcycsr - w2 = np.sqrt(s2 * r2) - h2 = np.sqrt(s2 / r2) - - # Linear interpolation deltas - dx = (x2 - x1) / time_gap - dy = (y2 - y1) / time_gap - dw = (w2 - w1) / time_gap - dh = (h2 - h1) / time_gap + """ORU interpolation for XCYCSR representation. + + Interpolates in XYXY space for more natural motion, then converts + to XCYCSR for Kalman filter update. + """ + last_xyxy = self.last_observation + new_xyxy = new_bbox + + # Linear interpolation in XYXY space + delta = (new_xyxy - last_xyxy) / time_gap for i in range(1, time_gap + 1): - x = x1 + i * dx - y = y1 + i * dy - w = w1 + i * dw - h = h1 + i * dh - - # Convert back to (x, y, s, r) - s = w * h - r = w / h - virtual_obs = np.array([x, y, s, r]).reshape((4, 1)) + virtual_xyxy = last_xyxy + i * delta + # Convert to XCYCSR for Kalman filter + virtual_obs = xyxy_to_xcycsr(virtual_xyxy).reshape((4, 1)) if i < time_gap: self.kalman_filter.predict() From 0da45f5b8ff01c9fe0ce134bf1ba5c5fa2c18e03 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 10 Feb 2026 11:09:15 -0300 Subject: [PATCH 28/87] rollback interpolation in xyxy system --- trackers/core/ocsort/tracklet.py | 41 ++++++++++++++++++++++---------- 1 file changed, 28 insertions(+), 13 deletions(-) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 8e2a80a8..31084fb4 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -206,21 +206,36 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: self._frozen_state = None def _unfreeze_xcycsr(self, new_bbox: np.ndarray, time_gap: int) -> None: - """ORU interpolation for XCYCSR representation. - - Interpolates in XYXY space for more natural motion, then converts - to XCYCSR for Kalman filter update. - """ - last_xyxy = self.last_observation - new_xyxy = new_bbox - - # Linear interpolation in XYXY space - delta = (new_xyxy - last_xyxy) / time_gap + """ORU interpolation for XCYCSR representation.""" + # Convert to (x, y, s, r) format + last_xcycsr = xyxy_to_xcycsr(self.last_observation) + new_xcycsr = xyxy_to_xcycsr(new_bbox) + + # Convert s, r back to w, h for interpolation + x1, y1, s1, r1 = last_xcycsr + w1 = np.sqrt(s1 * r1) + h1 = np.sqrt(s1 / r1) + + x2, y2, s2, r2 = new_xcycsr + w2 = np.sqrt(s2 * r2) + h2 = np.sqrt(s2 / r2) + + # Linear interpolation deltas + dx = (x2 - x1) / time_gap + dy = (y2 - y1) / time_gap + dw = (w2 - w1) / time_gap + dh = (h2 - h1) / time_gap for i in range(1, time_gap + 1): - virtual_xyxy = last_xyxy + i * delta - # Convert to XCYCSR for Kalman filter - virtual_obs = xyxy_to_xcycsr(virtual_xyxy).reshape((4, 1)) + x = x1 + i * dx + y = y1 + i * dy + w = w1 + i * dw + h = h1 + i * dh + + # Convert back to (x, y, s, r) + s = w * h + r = w / h + virtual_obs = np.array([x, y, s, r]).reshape((4, 1)) if i < time_gap: self.kalman_filter.predict() From 0480a1886e8afd3960802712a30aa384299f1343 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 11 Feb 2026 09:55:38 -0300 Subject: [PATCH 29/87] kf annotations fix and removed example --- trackers/utils/kalman_filter.py | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/trackers/utils/kalman_filter.py b/trackers/utils/kalman_filter.py index 7050efc7..5789f882 100644 --- a/trackers/utils/kalman_filter.py +++ b/trackers/utils/kalman_filter.py @@ -1,4 +1,4 @@ -from copy import deepcopy +from __future__ import annotations import numpy as np from numpy.typing import NDArray @@ -23,14 +23,6 @@ class KalmanFilter: P_prior: Prior covariance (after predict, before update). x_post: Posterior state estimate (after update). P_post: Posterior covariance (after update). - - Example: - >>> kf = KalmanFilter(dim_x=4, dim_z=2) - >>> kf.x = np.array([[0], [0], [0], [0]]) # initial state - >>> kf.F = np.eye(4) # state transition - >>> kf.H = np.array([[1, 0, 0, 0], [0, 1, 0, 0]]) # measurement - >>> kf.predict() - >>> kf.update(np.array([[1], [2]])) """ def __init__(self, dim_x: int, dim_z: int) -> None: From 7e2cfaf68dfece18e730e1eff83fa6da8e3bcbd4 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 11 Feb 2026 10:47:56 -0300 Subject: [PATCH 30/87] added oc-sort final numbers --- docs/index.md | 4 ++-- docs/trackers/comparison.md | 3 +++ docs/trackers/ocsort.md | 11 +++-------- 3 files changed, 8 insertions(+), 10 deletions(-) diff --git a/docs/index.md b/docs/index.md index 61c9b6cd..f8537091 100644 --- a/docs/index.md +++ b/docs/index.md @@ -76,8 +76,8 @@ You can install and use `trackers` in a [**Python>=3.10**](https://www.python.or | Algorithm | MOT17 HOTA | MOT17 IDF1 | MOT17 MOTA | SportsMOT HOTA | SoccerNet HOTA | | :-------: | :--------: | :--------: | :--------: | :------------: | :------------: | | SORT | 58.4 | 69.9 | 67.2 | 70.9 | 81.6 | -| ByteTrack | **60.1** | **73.2** | **74.1** | **73.0** | **84.0** | -| OC-SORT | — | — | — | — | — | +| ByteTrack | 60.1 | 73.2 | *74.1 | **73.0** | **84.0** | +| OC-SORT | **61.9** | **76.1** | **76.7** | 71.5 | 78.6 | | BoT-SORT | — | — | — | — | — | | McByte | — | — | — | — | — | diff --git a/docs/trackers/comparison.md b/docs/trackers/comparison.md index 0e985a28..435c0202 100644 --- a/docs/trackers/comparison.md +++ b/docs/trackers/comparison.md @@ -10,6 +10,7 @@ Pedestrian tracking with crowded scenes and frequent occlusions. Strongly tests | :-------: | :--: | :--: | :--: | | SORT | 58.4 | 69.9 | 67.2 | | ByteTrack | 60.1 | 73.2 | 74.1 | +| OC-SORT | 61.9 | 76.1 | 76.7 | ## SportsMOT @@ -19,6 +20,7 @@ Sports broadcast tracking with fast motion, camera pans, and similar-looking tar | :-------: | :--: | :--: | :--: | | SORT | 70.9 | 68.9 | 95.7 | | ByteTrack | 73.0 | 72.5 | 96.4 | +| OC-SORT | 71.5 | 71.2 | 95.2 | ## SoccerNet-tracking @@ -28,3 +30,4 @@ Long sequences with dense interactions and partial occlusions. Tests long-term I | :-------: | :--: | :--: | :--: | | SORT | 81.6 | 76.2 | 95.1 | | ByteTrack | 84.0 | 78.1 | 97.8 | +| OC-SORT | 78.6 | 72.7 | 94.5 | diff --git a/docs/trackers/ocsort.md b/docs/trackers/ocsort.md index ba9dbec9..48e2614f 100644 --- a/docs/trackers/ocsort.md +++ b/docs/trackers/ocsort.md @@ -11,13 +11,13 @@ It recognizes limitations from SORT and the linear motion assumption of the Kalm mechanisms help having better Kalman Filter parameters after an occlusion, add a term to the association process to incorporate how consistent is the direction with the new association with respect to the tracks' previous direction and add a second-stage association step between the last observation of unmatched tracks and the unmatched observations after the usual association to attempt to recover tracks that were lost due to object stopping or short-term occlusion. -## Benchmarks +## Comparison -For comparisons with other trackers, plus full details on the datasets and evaluation metrics used, see the [benchmarks](../learn/benchmarks.md) page. +For comparisons with other trackers, plus dataset context and evaluation details, see the [tracker comparison](comparison.md) page. | Dataset | HOTA | IDF1 | MOTA | |:---------:|:----:|:----:|:----:| -| MOT17 | ---- | ---- | ---- | +| MOT17 | 61.9 | 76.1 | 76.7 | | SportsMOT | 71.5 | 71.2 | 95.2 | | SoccerNet | 78.6 | 72.7 | 94.5 | @@ -138,8 +138,3 @@ These examples use OpenCV for decoding and display. Replace ` video_capture.release() cv2.destroyAllWindows() ``` - - -## API - -::: trackers.core.ocsort.tracker.OCSORTTracker From 2cfd0e7df58f4476d3df5456fe97b8b4e7ba052f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 11 Feb 2026 13:48:13 +0000 Subject: [PATCH 31/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/index.md | 6 +++--- docs/trackers/comparison.md | 4 ++-- docs/trackers/ocsort.md | 14 ++++++++++---- 3 files changed, 15 insertions(+), 9 deletions(-) diff --git a/docs/index.md b/docs/index.md index f8537091..0712b23f 100644 --- a/docs/index.md +++ b/docs/index.md @@ -57,7 +57,7 @@ You can install and use `trackers` in a [**Python>=3.10**](https://www.python.or [:simple-googlecolab: Run Google Colab](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-bytetrack-tracker.ipynb) -- **How to Track Objects with OC-SORT** +- **How to Track Objects with OC-SORT** --- @@ -76,8 +76,8 @@ You can install and use `trackers` in a [**Python>=3.10**](https://www.python.or | Algorithm | MOT17 HOTA | MOT17 IDF1 | MOT17 MOTA | SportsMOT HOTA | SoccerNet HOTA | | :-------: | :--------: | :--------: | :--------: | :------------: | :------------: | | SORT | 58.4 | 69.9 | 67.2 | 70.9 | 81.6 | -| ByteTrack | 60.1 | 73.2 | *74.1 | **73.0** | **84.0** | -| OC-SORT | **61.9** | **76.1** | **76.7** | 71.5 | 78.6 | +| ByteTrack | 60.1 | 73.2 | \*74.1 | **73.0** | **84.0** | +| OC-SORT | **61.9** | **76.1** | **76.7** | 71.5 | 78.6 | | BoT-SORT | — | — | — | — | — | | McByte | — | — | — | — | — | diff --git a/docs/trackers/comparison.md b/docs/trackers/comparison.md index 435c0202..bc9fc645 100644 --- a/docs/trackers/comparison.md +++ b/docs/trackers/comparison.md @@ -20,7 +20,7 @@ Sports broadcast tracking with fast motion, camera pans, and similar-looking tar | :-------: | :--: | :--: | :--: | | SORT | 70.9 | 68.9 | 95.7 | | ByteTrack | 73.0 | 72.5 | 96.4 | -| OC-SORT | 71.5 | 71.2 | 95.2 | +| OC-SORT | 71.5 | 71.2 | 95.2 | ## SoccerNet-tracking @@ -30,4 +30,4 @@ Long sequences with dense interactions and partial occlusions. Tests long-term I | :-------: | :--: | :--: | :--: | | SORT | 81.6 | 76.2 | 95.1 | | ByteTrack | 84.0 | 78.1 | 97.8 | -| OC-SORT | 78.6 | 72.7 | 94.5 | +| OC-SORT | 78.6 | 72.7 | 94.5 | diff --git a/docs/trackers/ocsort.md b/docs/trackers/ocsort.md index 48e2614f..180da3c1 100644 --- a/docs/trackers/ocsort.md +++ b/docs/trackers/ocsort.md @@ -16,7 +16,7 @@ due to object stopping or short-term occlusion. For comparisons with other trackers, plus dataset context and evaluation details, see the [tracker comparison](comparison.md) page. | Dataset | HOTA | IDF1 | MOTA | -|:---------:|:----:|:----:|:----:| +| :-------: | :--: | :--: | :--: | | MOT17 | 61.9 | 76.1 | 76.7 | | SportsMOT | 71.5 | 71.2 | 95.2 | | SoccerNet | 78.6 | 72.7 | 94.5 | @@ -53,7 +53,9 @@ These examples use OpenCV for decoding and display. Replace ` detections = tracker.update(detections) annotated_frame = box_annotator.annotate(frame_bgr, detections) - annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + annotated_frame = label_annotator.annotate( + annotated_frame, detections, labels=detections.tracker_id + ) cv2.imshow("RF-DETR + OC-SORT", annotated_frame) if cv2.waitKey(1) & 0xFF == ord("q"): @@ -91,7 +93,9 @@ These examples use OpenCV for decoding and display. Replace ` detections = tracker.update(detections) annotated_frame = box_annotator.annotate(frame_bgr, detections) - annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + annotated_frame = label_annotator.annotate( + annotated_frame, detections, labels=detections.tracker_id + ) cv2.imshow("RF-DETR + OC-SORT", annotated_frame) if cv2.waitKey(1) & 0xFF == ord("q"): @@ -129,7 +133,9 @@ These examples use OpenCV for decoding and display. Replace ` detections = tracker.update(detections) annotated_frame = box_annotator.annotate(frame_bgr, detections) - annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + annotated_frame = label_annotator.annotate( + annotated_frame, detections, labels=detections.tracker_id + ) cv2.imshow("RF-DETR + OC-SORT", annotated_frame) if cv2.waitKey(1) & 0xFF == ord("q"): From 990bdd2b2624035bf5a61d370e43ba1f33cce7d2 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 11 Feb 2026 11:09:15 -0300 Subject: [PATCH 32/87] readme with final numbers, utils with _ and ocsort in docs/api --- README.md | 4 ++-- docs/api/trackers.md | 4 ++++ trackers/core/ocsort/utils.py | 16 ++++++++-------- 3 files changed, 14 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index a454bb09..8cb80c4e 100644 --- a/README.md +++ b/README.md @@ -49,8 +49,8 @@ trackers track --source source.mp4 --output output.mp4 --model rfdetr-nano --tra | Algorithm | MOT17 HOTA | MOT17 IDF1 | MOT17 MOTA | SportsMOT HOTA | SoccerNet HOTA | | :-------: | :--------: | :--------: | :--------: | :------------: | :------------: | | SORT | 58.4 | 69.9 | 67.2 | 70.9 | 81.6 | -| ByteTrack | **60.1** | **73.2** | **74.1** | **73.0** | **84.0** | -| OC-SORT | — | — | — | — | — | +| ByteTrack | 60.1 | 73.2 | *74.1 | **73.0** | **84.0** | +| OC-SORT | **61.9** | **76.1** | **76.7** | 71.5 | 78.6 | | BoT-SORT | — | — | — | — | — | | McByte | — | — | — | — | — | diff --git a/docs/api/trackers.md b/docs/api/trackers.md index 7ef8b192..d3553f21 100644 --- a/docs/api/trackers.md +++ b/docs/api/trackers.md @@ -7,3 +7,7 @@ ## ByteTrack ::: trackers.core.bytetrack.tracker.ByteTrackTracker + +## OC-SORT + +::: trackers.core.ocsort.tracker.OCSORTTracker \ No newline at end of file diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 29eb412d..dde8e421 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -3,7 +3,7 @@ # Copyright (c) 2026 Roboflow. All Rights Reserved. # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ -# Copied from OC-SORT https://github.com/noahcao/OC_SORT/ +# Modified and adapted from OC-SORT https://github.com/noahcao/OC_SORT/ # Licensed under the MIT License [see LICENSE for details] # ------------------------------------------------------------------------ @@ -16,7 +16,7 @@ from trackers.core.ocsort.tracklet import OCSORTTracklet -def speed_direction(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: +def _speed_direction(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: """Compute normalized direction vector between two bounding box centers. Args: @@ -33,7 +33,7 @@ def speed_direction(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: return speed / norm -def build_direction_consistency_matrix( +def _build_direction_consistency_matrix( tracklets: list[OCSORTTracklet], detection_boxes: np.ndarray, ) -> np.ndarray: @@ -60,13 +60,13 @@ def build_direction_consistency_matrix( if tracklet.previous_to_last_observation is None: continue last_observation = tracklet.last_observation - tracklet_speed = speed_direction( + tracklet_speed = _speed_direction( tracklet.previous_to_last_observation, last_observation ) for d in range(n_detections): detection_box = detection_boxes[d] - association_speed = speed_direction(last_observation, detection_box) + association_speed = _speed_direction(last_observation, detection_box) cos_sim = np.dot(tracklet_speed, association_speed) cos_sim = np.clip(cos_sim, -1.0, 1.0) @@ -76,7 +76,7 @@ def build_direction_consistency_matrix( return direction_consistency_matrix -def speed_direction_batch( +def _speed_direction_batch( dets: np.ndarray, tracks: np.ndarray ) -> tuple[np.ndarray, np.ndarray]: """Compute normalized direction vectors from tracks to detections in batch. @@ -125,7 +125,7 @@ def build_direction_consistency_matrix_batch( # Compute tracklet velocities (from previous_to_last -> last_observation) velocities = np.array( [ - speed_direction( + _speed_direction( tracklet.previous_to_last_observation, tracklet.last_observation ) if tracklet.previous_to_last_observation is not None @@ -138,7 +138,7 @@ def build_direction_consistency_matrix_batch( last_obs = np.array([tracklet.last_observation for tracklet in tracklets]) # Compute association directions (from last_observation -> detection) in batch - Y, X = speed_direction_batch( + Y, X = _speed_direction_batch( detection_boxes, last_obs ) # (n_tracklets, n_detections) From cf5ae7e66dc02da512bd1d0c7ccf0ceed47f57d9 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 11 Feb 2026 11:11:04 -0300 Subject: [PATCH 33/87] readme with final numbers, utils with _ and ocsort in docs/api --- README.md | 4 ++-- docs/api/trackers.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 8cb80c4e..527c4dc9 100644 --- a/README.md +++ b/README.md @@ -49,8 +49,8 @@ trackers track --source source.mp4 --output output.mp4 --model rfdetr-nano --tra | Algorithm | MOT17 HOTA | MOT17 IDF1 | MOT17 MOTA | SportsMOT HOTA | SoccerNet HOTA | | :-------: | :--------: | :--------: | :--------: | :------------: | :------------: | | SORT | 58.4 | 69.9 | 67.2 | 70.9 | 81.6 | -| ByteTrack | 60.1 | 73.2 | *74.1 | **73.0** | **84.0** | -| OC-SORT | **61.9** | **76.1** | **76.7** | 71.5 | 78.6 | +| ByteTrack | 60.1 | 73.2 | \*74.1 | **73.0** | **84.0** | +| OC-SORT | **61.9** | **76.1** | **76.7** | 71.5 | 78.6 | | BoT-SORT | — | — | — | — | — | | McByte | — | — | — | — | — | diff --git a/docs/api/trackers.md b/docs/api/trackers.md index d3553f21..74eb9560 100644 --- a/docs/api/trackers.md +++ b/docs/api/trackers.md @@ -10,4 +10,4 @@ ## OC-SORT -::: trackers.core.ocsort.tracker.OCSORTTracker \ No newline at end of file +::: trackers.core.ocsort.tracker.OCSORTTracker From 80753fb2b1eefc92be18420da2352535c6f40c0b Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 11 Feb 2026 11:17:35 -0300 Subject: [PATCH 34/87] added headers and changed parameters name high_conf_det_threshold -> high_conf_detect_threshold --- trackers/core/ocsort/tracker.py | 16 ++++++++++++---- trackers/core/ocsort/tracklet.py | 6 ++++++ 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 8385bd39..bfc8b198 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -1,3 +1,9 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + import numpy as np import supervision as sv from scipy.optimize import linear_sum_assignment @@ -41,7 +47,7 @@ class OCSORTTracker(BaseTracker): existing tracks. direction_consistency_weight: Weight for inertia term in association cost. Higher values give more importance to the angle difference between the motion direction and the association direction. - high_conf_det_threshold: Confidence threshold to consider a detection as high confidence. If a detection has + high_conf_detect_threshold: Confidence threshold to consider a detection as high confidence. If a detection has confidence lower than this threshold, it will not be considered for association. """ # noqa: E501 @@ -55,7 +61,7 @@ def __init__( minimum_consecutive_frames: int = 3, minimum_iou_threshold: float = 0.3, direction_consistency_weight: float = 0.2, - high_conf_det_threshold: float = 0.6, + high_conf_detect_threshold: float = 0.6, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -64,7 +70,7 @@ def __init__( self.minimum_consecutive_frames = minimum_consecutive_frames self.minimum_iou_threshold = minimum_iou_threshold self.direction_consistency_weight = direction_consistency_weight - self.high_conf_det_threshold = high_conf_det_threshold + self.high_conf_detect_threshold = high_conf_detect_threshold self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 @@ -146,7 +152,9 @@ def update(self, detections: sv.Detections) -> sv.Detections: detections.tracker_id = np.array([], dtype=int) return detections - detections = detections[detections.confidence >= self.high_conf_det_threshold] + detections = detections[ + detections.confidence >= self.high_conf_detect_threshold + ] updated_detections: list[ sv.Detections diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 31084fb4..96cabd45 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -1,3 +1,9 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + from __future__ import annotations from enum import Enum From 8e07a25a77d5c2b506f2414645b4b95551194c19 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 11 Feb 2026 11:20:42 -0300 Subject: [PATCH 35/87] rollback parameter name changed --- trackers/core/ocsort/tracker.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index bfc8b198..2f97997f 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -47,7 +47,7 @@ class OCSORTTracker(BaseTracker): existing tracks. direction_consistency_weight: Weight for inertia term in association cost. Higher values give more importance to the angle difference between the motion direction and the association direction. - high_conf_detect_threshold: Confidence threshold to consider a detection as high confidence. If a detection has + high_conf_det_threshold: Confidence threshold to consider a detection as high confidence. If a detection has confidence lower than this threshold, it will not be considered for association. """ # noqa: E501 @@ -61,7 +61,7 @@ def __init__( minimum_consecutive_frames: int = 3, minimum_iou_threshold: float = 0.3, direction_consistency_weight: float = 0.2, - high_conf_detect_threshold: float = 0.6, + high_conf_det_threshold: float = 0.6, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -70,7 +70,7 @@ def __init__( self.minimum_consecutive_frames = minimum_consecutive_frames self.minimum_iou_threshold = minimum_iou_threshold self.direction_consistency_weight = direction_consistency_weight - self.high_conf_detect_threshold = high_conf_detect_threshold + self.high_conf_det_threshold = high_conf_det_threshold self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 @@ -152,9 +152,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: detections.tracker_id = np.array([], dtype=int) return detections - detections = detections[ - detections.confidence >= self.high_conf_detect_threshold - ] + detections = detections[detections.confidence >= self.high_conf_det_threshold] updated_detections: list[ sv.Detections From a967b61fd4711c5088061dc0ea3ebf040c7d66ba Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 11 Feb 2026 11:27:43 -0300 Subject: [PATCH 36/87] removed typings from docstring --- trackers/core/ocsort/tracker.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 2f97997f..27cf4486 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -88,8 +88,12 @@ def _get_associated_indices( direction_consistency_matrix: Direction of the tracklet consistency cost matrix. Returns: - tuple[list[tuple[int, int]], list[int], list[int]]: Matched indices, - unmatched tracks, unmatched detections. + matched_indices: List of (track_index, detection_index) tuples for + successful associations that meet the IOU threshold. + unmatched_tracks: list of track indices that were not matched + to any detection. + unmatched_detections: list of detection indices that were not + matched to any track. """ # noqa: E501 matched_indices = [] n_tracks, n_detections = iou_matrix.shape @@ -110,8 +114,8 @@ def _get_associated_indices( return ( matched_indices, - sorted(list(unmatched_tracks)), - sorted(list(unmatched_detections)), + list(unmatched_tracks), + list(unmatched_detections), ) def _spawn_new_tracklets( @@ -143,7 +147,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: detections: The latest set of object detections from a frame. Returns: - sv.Detections: A copy of the input detections, augmented with assigned + updated_detections: A copy of the input detections, augmented with assigned `tracklet_id` for each successfully tracked object. Detections not associated with a track will not have a `tracklet_id`. """ From be508abe6f46b1725aa2ebbeefeb08ded21e307a Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 12 Feb 2026 12:11:16 -0300 Subject: [PATCH 37/87] added change from PR to prevent in place mutation: --- trackers/core/ocsort/tracker.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 27cf4486..a6ed10f5 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -4,6 +4,8 @@ # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ +from copy import deepcopy + import numpy as np import supervision as sv from scipy.optimize import linear_sum_assignment @@ -153,8 +155,9 @@ def update(self, detections: sv.Detections) -> sv.Detections: """ if len(self.tracks) == 0 and len(detections) == 0: - detections.tracker_id = np.array([], dtype=int) - return detections + result = deepcopy(detections) + result.tracker_id = np.array([], dtype=int) + return result detections = detections[detections.confidence >= self.high_conf_det_threshold] From 58f793e075655095d6ea92e0cca741dd13c8a979 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Mon, 16 Feb 2026 14:11:27 -0300 Subject: [PATCH 38/87] trying to add delta_t --- trackers/core/ocsort/tracker.py | 10 +++- trackers/core/ocsort/tracklet.py | 55 +++++++++++++++++++++- trackers/core/ocsort/utils.py | 78 ++++++++++++++++++++++++-------- 3 files changed, 121 insertions(+), 22 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index a6ed10f5..5e694b47 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -51,6 +51,10 @@ class OCSORTTracker(BaseTracker): to the angle difference between the motion direction and the association direction. high_conf_det_threshold: Confidence threshold to consider a detection as high confidence. If a detection has confidence lower than this threshold, it will not be considered for association. + delta_t: Number of timesteps back to look for velocity/direction estimation. + Higher values use observations further in the past to compute motion + direction, providing more stable velocity estimates during occlusion. + Default is 3 (matching the original OC-SORT paper). """ # noqa: E501 @@ -64,6 +68,7 @@ def __init__( minimum_iou_threshold: float = 0.3, direction_consistency_weight: float = 0.2, high_conf_det_threshold: float = 0.6, + delta_t: int = 3, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -73,6 +78,7 @@ def __init__( self.minimum_iou_threshold = minimum_iou_threshold self.direction_consistency_weight = direction_consistency_weight self.high_conf_det_threshold = high_conf_det_threshold + self.delta_t = delta_t self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 @@ -135,7 +141,9 @@ def _spawn_new_tracklets( form [x1, y1, x2, y2]. """ for detection_idx in unmatched_detections: - new_tracker = OCSORTTracklet(detections.xyxy[detection_idx]) + new_tracker = OCSORTTracklet( + detections.xyxy[detection_idx], delta_t=self.delta_t + ) self.tracks.append(new_tracker) def update(self, detections: sv.Detections) -> sv.Detections: diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 96cabd45..3329ca61 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -50,6 +50,9 @@ class OCSORTTracklet: time_since_update: Frames since last observation. last_observation: Last observed bounding box [x1, y1, x2, y2]. previous_to_last_observation: Second-to-last observation for velocity. + observations: Dict mapping age to observed bbox for delta_t lookback. + velocity: Normalized direction vector computed with delta_t lookback. + delta_t: Number of timesteps back to look for velocity estimation. state_repr: The state representation being used. """ @@ -59,12 +62,16 @@ def __init__( self, initial_bbox: np.ndarray, state_repr: StateRepresentation = StateRepresentation.XCYCSR, + delta_t: int = 3, ) -> None: """Initialize tracklet with first detection. Args: initial_bbox: Initial bounding box [x1, y1, x2, y2]. state_repr: State representation to use (XCYCSR or XYXY). + delta_t: Number of timesteps back to look for velocity estimation. + Higher values use observations further in the past to estimate + motion direction, providing more stable velocity estimates. """ self.age = 0 self.state_repr = state_repr @@ -75,9 +82,12 @@ def __init__( else: self._init_xyxy_filter(initial_bbox) - # Observation history for ORU + # Observation history for ORU and delta_t + self.delta_t = delta_t self.last_observation = initial_bbox self.previous_to_last_observation: np.ndarray | None = None + self.observations: dict[int, np.ndarray] = {0: initial_bbox} + self.velocity: np.ndarray | None = None # Track ID can be initialized before mature in oc-sort # it is assigned if the frame number is less than minimum_consecutive_frames @@ -262,16 +272,58 @@ def _unfreeze_xyxy(self, new_bbox: np.ndarray, time_gap: int) -> None: self.kalman_filter.predict() self.kalman_filter.update(virtual_obs) + def get_k_previous_obs(self) -> np.ndarray | None: + """Get observation from delta_t steps ago. + + Looks back up to delta_t timesteps in the observation history. + Falls back to the most recent observation if none found in the window. + + Returns: + The observation from delta_t steps ago, or most recent if not found, + or None if no observations exist. + """ + if len(self.observations) == 0: + return None + for i in range(self.delta_t): + dt = self.delta_t - i + if self.age - dt in self.observations: + return self.observations[self.age - dt] + max_age = max(self.observations.keys()) + return self.observations[max_age] + + @staticmethod + def _compute_velocity(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: + """Compute normalized direction vector between two bounding box centers. + + Args: + bbox1: First bounding box [x1, y1, x2, y2]. + bbox2: Second bounding box [x1, y1, x2, y2]. + + Returns: + Normalized direction vector [dy, dx]. + """ + cx1, cy1 = (bbox1[0] + bbox1[2]) / 2.0, (bbox1[1] + bbox1[3]) / 2.0 + cx2, cy2 = (bbox2[0] + bbox2[2]) / 2.0, (bbox2[1] + bbox2[3]) / 2.0 + speed = np.array([cy2 - cy1, cx2 - cx1]) + norm = np.sqrt((cy2 - cy1) ** 2 + (cx2 - cx1) ** 2) + 1e-6 + return speed / norm + def update(self, bbox: np.ndarray | None) -> None: """Update tracklet with new observation. Handles ORU: if track was lost and now observed again, generates virtual trajectory to smooth the transition. + Computes velocity using observation from delta_t steps ago. Args: bbox: Bounding box [x1, y1, x2, y2] or None for no observation. """ if bbox is not None: + # Compute velocity using k_previous_obs (delta_t lookback) + previous_box = self.get_k_previous_obs() + if previous_box is not None: + self.velocity = self._compute_velocity(previous_box, bbox) + # Check if we need to unfreeze (was lost, now observed) if not self._observed and self._frozen_state is not None: self._unfreeze(bbox) @@ -284,6 +336,7 @@ def update(self, bbox: np.ndarray | None) -> None: self.number_of_successful_consecutive_updates += 1 self.previous_to_last_observation = self.last_observation self.last_observation = bbox + self.observations[self.age] = bbox else: # No observation - freeze state if this is first miss if self._observed: diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index dde8e421..d57af00a 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -7,6 +7,8 @@ # Licensed under the MIT License [see LICENSE for details] # ------------------------------------------------------------------------ +from __future__ import annotations + from copy import deepcopy from typing import Sequence, cast @@ -16,6 +18,33 @@ from trackers.core.ocsort.tracklet import OCSORTTracklet +def k_previous_obs( + observations: dict[int, np.ndarray], cur_age: int, delta_t: int +) -> np.ndarray | None: + """Get observation from delta_t steps ago from the observations dict. + + Looks back up to delta_t timesteps. Falls back to the most recent + observation if none found in the lookback window. + + Args: + observations: Dict mapping age to observed bbox. + cur_age: Current age of the tracklet. + delta_t: Number of timesteps to look back. + + Returns: + The observation from delta_t steps ago, or the most recent one, + or None if no observations exist. + """ + if len(observations) == 0: + return None + for i in range(delta_t): + dt = delta_t - i + if cur_age - dt in observations: + return observations[cur_age - dt] + max_age = max(observations.keys()) + return observations[max_age] + + def _speed_direction(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: """Compute normalized direction vector between two bounding box centers. @@ -43,6 +72,10 @@ def _build_direction_consistency_matrix( Note: This is the non-batch version kept for reference, interpretability and testing purposes. Use `build_direction_consistency_matrix_batch` for production. + Uses tracklet.velocity (computed with delta_t lookback) and k_previous_obs + as the reference point for association direction, matching the original + OC-SORT implementation. + Args: tracklets: List of OCSORTTracklet objects. detection_boxes: Detection bounding boxes [x1, y1, x2, y2]. @@ -57,16 +90,18 @@ def _build_direction_consistency_matrix( ) for t, tracklet in enumerate(tracklets): - if tracklet.previous_to_last_observation is None: + if tracklet.velocity is None: + continue + + k_obs = tracklet.get_k_previous_obs() + if k_obs is None: continue - last_observation = tracklet.last_observation - tracklet_speed = _speed_direction( - tracklet.previous_to_last_observation, last_observation - ) + + tracklet_speed = tracklet.velocity for d in range(n_detections): detection_box = detection_boxes[d] - association_speed = _speed_direction(last_observation, detection_box) + association_speed = _speed_direction(k_obs, detection_box) cos_sim = np.dot(tracklet_speed, association_speed) cos_sim = np.clip(cos_sim, -1.0, 1.0) @@ -106,8 +141,9 @@ def build_direction_consistency_matrix_batch( ) -> np.ndarray: """Build direction consistency cost matrix (OCM) in batch - vectorized version. - Computes similarity between tracklet velocity vectors and potential - association directions. Used in OC-SORT for motion-aware association. + Computes similarity between tracklet velocity vectors (computed with delta_t + lookback) and potential association directions from k-previous observations. + Used in OC-SORT for motion-aware association. Args: tracklets: List of OCSORTTracklet objects. @@ -122,24 +158,26 @@ def build_direction_consistency_matrix_batch( if n_tracklets == 0 or n_detections == 0: return np.zeros((n_tracklets, n_detections), dtype=np.float32) - # Compute tracklet velocities (from previous_to_last -> last_observation) + # Use precomputed velocities from tracklets (computed with delta_t lookback) velocities = np.array( [ - _speed_direction( - tracklet.previous_to_last_observation, tracklet.last_observation - ) - if tracklet.previous_to_last_observation is not None - else np.array([0.0, 0.0]) + tracklet.velocity if tracklet.velocity is not None else np.array([0.0, 0.0]) for tracklet in tracklets ] ) - # Get last observations as array for batch direction computation - last_obs = np.array([tracklet.last_observation for tracklet in tracklets]) + # Get k-previous observations as reference for association direction + k_obs_list = [tracklet.get_k_previous_obs() for tracklet in tracklets] + k_observations = np.array( + [ + obs if obs is not None else tracklet.last_observation + for obs, tracklet in zip(k_obs_list, tracklets) + ] + ) - # Compute association directions (from last_observation -> detection) in batch + # Compute association directions (from k_observations -> detection) in batch Y, X = _speed_direction_batch( - detection_boxes, last_obs + detection_boxes, k_observations ) # (n_tracklets, n_detections) # Expand velocities for broadcasting @@ -153,9 +191,9 @@ def build_direction_consistency_matrix_batch( diff_angle = np.arccos(diff_angle_cos) angle_diff_cost = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi - # Mask out tracklets without previous observation + # Mask out tracklets without velocity (no previous observation) valid_mask = np.array( - [tracklet.previous_to_last_observation is not None for tracklet in tracklets], + [tracklet.velocity is not None for tracklet in tracklets], dtype=np.float32, )[:, np.newaxis] # (n_tracklets, 1) From f1ac1636c97c921d765226d5d2224e7f033ad40b Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 17 Feb 2026 09:37:27 -0300 Subject: [PATCH 39/87] trying fix to delta_t: velocity not computed until second match, frame count<=minimum_consecutive_frames (before was <, now it is aligned with original repo) --- trackers/core/ocsort/tracklet.py | 11 +++++++++-- trackers/core/ocsort/utils.py | 2 +- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 3329ca61..06eb5e6a 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -86,7 +86,7 @@ def __init__( self.delta_t = delta_t self.last_observation = initial_bbox self.previous_to_last_observation: np.ndarray | None = None - self.observations: dict[int, np.ndarray] = {0: initial_bbox} + self.observations: dict[int, np.ndarray] = {} self.velocity: np.ndarray | None = None # Track ID can be initialized before mature in oc-sort @@ -319,7 +319,9 @@ def update(self, bbox: np.ndarray | None) -> None: bbox: Bounding box [x1, y1, x2, y2] or None for no observation. """ if bbox is not None: - # Compute velocity using k_previous_obs (delta_t lookback) + # Compute velocity only after the track has been observed at least once + # (matches original OC-SORT: velocity is None until 2nd match) + previous_box = self.get_k_previous_obs() if previous_box is not None: self.velocity = self._compute_velocity(previous_box, bbox) @@ -350,6 +352,11 @@ def predict(self) -> np.ndarray: Returns: Predicted bounding box [x1, y1, x2, y2]. """ + # If predicted scale would go negative, zero out scale velocity + if self.state_repr == StateRepresentation.XCYCSR: + if (self.kalman_filter.x[6] + self.kalman_filter.x[2]) <= 0: + self.kalman_filter.x[6] *= 0.0 + self.kalman_filter.predict() self.age += 1 diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index d57af00a..76e47f30 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -229,7 +229,7 @@ def add_track_id_detections( is_mature = ( track.number_of_successful_consecutive_updates >= minimum_consecutive_frames ) - if frame_count < minimum_consecutive_frames: + if frame_count <= minimum_consecutive_frames: if track.time_since_update == 0: if track.tracker_id == -1: track.tracker_id = OCSORTTracklet.get_next_tracker_id() From 44ec83d353510f3d6237a06f048333275e47309e Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 17 Feb 2026 12:33:07 -0300 Subject: [PATCH 40/87] reverted <= change --- trackers/core/ocsort/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 76e47f30..d57af00a 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -229,7 +229,7 @@ def add_track_id_detections( is_mature = ( track.number_of_successful_consecutive_updates >= minimum_consecutive_frames ) - if frame_count <= minimum_consecutive_frames: + if frame_count < minimum_consecutive_frames: if track.time_since_update == 0: if track.tracker_id == -1: track.tracker_id = OCSORTTracklet.get_next_tracker_id() From fc8a2fd80d16dea08b3e7559ec33b6140350c958 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 17 Feb 2026 12:50:52 -0300 Subject: [PATCH 41/87] aligned to original initialization of time_since_update and changes in re-update cycle --- trackers/core/ocsort/tracklet.py | 47 ++++++++++++++++++++------------ trackers/core/ocsort/utils.py | 2 +- 2 files changed, 31 insertions(+), 18 deletions(-) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 06eb5e6a..31acb213 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -94,7 +94,7 @@ def __init__( self.tracker_id = -1 # Tracking counters - self.number_of_successful_consecutive_updates = 1 + self.number_of_successful_consecutive_updates = 0 self.time_since_update = 0 # ORU: saved state for freeze/unfreeze @@ -222,7 +222,13 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: self._frozen_state = None def _unfreeze_xcycsr(self, new_bbox: np.ndarray, time_gap: int) -> None: - """ORU interpolation for XCYCSR representation.""" + """ORU interpolation for XCYCSR representation. + + Generates time_gap predict+update cycles with virtual observations + interpolated from the last observation to the new bbox. The interpolation + factors go from 0 to (time_gap-1)/time_gap. The caller is responsible + for the final real update at factor 1.0. + """ # Convert to (x, y, s, r) format last_xcycsr = xyxy_to_xcycsr(self.last_observation) new_xcycsr = xyxy_to_xcycsr(new_bbox) @@ -242,35 +248,39 @@ def _unfreeze_xcycsr(self, new_bbox: np.ndarray, time_gap: int) -> None: dw = (w2 - w1) / time_gap dh = (h2 - h1) / time_gap - for i in range(1, time_gap + 1): - x = x1 + i * dx - y = y1 + i * dy - w = w1 + i * dw - h = h1 + i * dh + for i in range(time_gap): + x = x1 + (i + 1) * dx + y = y1 + (i + 1) * dy + w = w1 + (i + 1) * dw + h = h1 + (i + 1) * dh # Convert back to (x, y, s, r) s = w * h r = w / h virtual_obs = np.array([x, y, s, r]).reshape((4, 1)) - if i < time_gap: - self.kalman_filter.predict() self.kalman_filter.update(virtual_obs) + if i < time_gap - 1: + self.kalman_filter.predict() def _unfreeze_xyxy(self, new_bbox: np.ndarray, time_gap: int) -> None: - """ORU interpolation for XYXY representation.""" + """ORU interpolation for XYXY representation. + + Same pattern as XCYCSR: time_gap predict+update cycles with factors + 0 to (time_gap-1)/time_gap. Caller does the final real update. + """ last_xyxy = self.last_observation new_xyxy = new_bbox # Linear interpolation deltas for each coordinate delta = (new_xyxy - last_xyxy) / time_gap - for i in range(1, time_gap + 1): - virtual_obs = (last_xyxy + i * delta).reshape((4, 1)) + for i in range(time_gap): + virtual_obs = (last_xyxy + (i + 1) * delta).reshape((4, 1)) - if i < time_gap: - self.kalman_filter.predict() self.kalman_filter.update(virtual_obs) + if i < time_gap - 1: + self.kalman_filter.predict() def get_k_previous_obs(self) -> np.ndarray | None: """Get observation from delta_t steps ago. @@ -329,9 +339,12 @@ def update(self, bbox: np.ndarray | None) -> None: # Check if we need to unfreeze (was lost, now observed) if not self._observed and self._frozen_state is not None: self._unfreeze(bbox) - else: - measurement = self._bbox_to_measurement(bbox) - self.kalman_filter.update(measurement) + + # Update KF with the real observation + # (after ORU this is the final update at the correct time step; + # without ORU this is the normal measurement update) + measurement = self._bbox_to_measurement(bbox) + self.kalman_filter.update(measurement) self._observed = True self.time_since_update = 0 diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index d57af00a..76e47f30 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -229,7 +229,7 @@ def add_track_id_detections( is_mature = ( track.number_of_successful_consecutive_updates >= minimum_consecutive_frames ) - if frame_count < minimum_consecutive_frames: + if frame_count <= minimum_consecutive_frames: if track.time_since_update == 0: if track.tracker_id == -1: track.tracker_id = OCSORTTracklet.get_next_tracker_id() From 154c8307c9fea0c3eda2562ab33cabe3c515f926 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 19 Feb 2026 12:04:55 -0300 Subject: [PATCH 42/87] added headers to utils --- trackers/utils/converters.py | 6 ++++++ trackers/utils/kalman_filter.py | 6 ++++++ 2 files changed, 12 insertions(+) diff --git a/trackers/utils/converters.py b/trackers/utils/converters.py index b1140cd4..798ce229 100644 --- a/trackers/utils/converters.py +++ b/trackers/utils/converters.py @@ -1,3 +1,9 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------- + import numpy as np diff --git a/trackers/utils/kalman_filter.py b/trackers/utils/kalman_filter.py index 5789f882..1579933a 100644 --- a/trackers/utils/kalman_filter.py +++ b/trackers/utils/kalman_filter.py @@ -1,3 +1,9 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------- + from __future__ import annotations import numpy as np From 09a62cb5d6cfedc1be3e8363b212babfc76516a1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 20 Feb 2026 13:23:42 +0000 Subject: [PATCH 43/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- trackers/core/ocsort/utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 76e47f30..043d4c00 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -9,8 +9,9 @@ from __future__ import annotations +from collections.abc import Sequence from copy import deepcopy -from typing import Sequence, cast +from typing import cast import numpy as np import supervision as sv From 2f593e166eb7c5760477816fc8836492d3fafa1c Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 20 Feb 2026 19:06:47 -0300 Subject: [PATCH 44/87] modular state representation --- trackers/core/ocsort/tracker.py | 7 +- trackers/core/ocsort/tracklet.py | 126 ++----------- trackers/utils/state_representations.py | 227 ++++++++++++++++++++++++ 3 files changed, 241 insertions(+), 119 deletions(-) create mode 100644 trackers/utils/state_representations.py diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 5e694b47..297a084e 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -18,6 +18,7 @@ get_iou_matrix, get_iou_matrix_between_boxes, ) +from trackers.utils.state_representations import XCYCSRStateRepresentation class OCSORTTracker(BaseTracker): @@ -79,10 +80,10 @@ def __init__( self.direction_consistency_weight = direction_consistency_weight self.high_conf_det_threshold = high_conf_det_threshold self.delta_t = delta_t - + self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 - + self.state_representation = XCYCSRStateRepresentation() def _get_associated_indices( self, iou_matrix: np.ndarray, @@ -142,7 +143,7 @@ def _spawn_new_tracklets( """ for detection_idx in unmatched_detections: new_tracker = OCSORTTracklet( - detections.xyxy[detection_idx], delta_t=self.delta_t + detections.xyxy[detection_idx], delta_t=self.delta_t, state_representation=self.state_representation ) self.tracks.append(new_tracker) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 31acb213..2e98fb72 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -6,8 +6,6 @@ from __future__ import annotations -from enum import Enum - import numpy as np from trackers.utils.converters import ( @@ -15,23 +13,7 @@ xyxy_to_xcycsr, ) from trackers.utils.kalman_filter import KalmanFilter - - -class StateRepresentation(Enum): - """Kalman filter state representation for bounding boxes. - - XCYCSR: Center-based (x_center, y_center, scale, aspect_ratio, vx, vy, vs) - - 7 state variables, aspect ratio is constant (no velocity) - - Used in original SORT/OC-SORT papers - - XYXY: Corner-based (x1, y1, x2, y2, vx1, vy1, vx2, vy2) - - 8 state variables, all coordinates have velocities - - More direct representation, potentially better for non-rigid objects - """ - - XCYCSR = "xcycsr" - XYXY = "xyxy" - +from trackers.utils.state_representations import BaseStateRepresentation, XCYCSRStateRepresentation class OCSORTTracklet: """Tracklet for OC-SORT tracker with ORU (Observation-centric Re-Update). @@ -61,7 +43,7 @@ class OCSORTTracklet: def __init__( self, initial_bbox: np.ndarray, - state_repr: StateRepresentation = StateRepresentation.XCYCSR, + state_repr: BaseStateRepresentation = XCYCSRStateRepresentation, delta_t: int = 3, ) -> None: """Initialize tracklet with first detection. @@ -77,10 +59,8 @@ def __init__( self.state_repr = state_repr # Initialize Kalman filter based on state representation - if state_repr == StateRepresentation.XCYCSR: - self._init_xcycsr_filter(initial_bbox) - else: - self._init_xyxy_filter(initial_bbox) + self.kalman_filter = self.state_repr.create_filter(initial_bbox) + # Observation history for ORU and delta_t self.delta_t = delta_t @@ -101,90 +81,6 @@ def __init__( self._frozen_state: dict | None = None self._observed = True - def _init_xcycsr_filter(self, initial_bbox: np.ndarray) -> None: - """Initialize Kalman filter with XCYCSR state representation. - - State: [x_center, y_center, scale, aspect_ratio, vx, vy, vs] - Measurement: [x_center, y_center, scale, aspect_ratio] - """ - self.kalman_filter = KalmanFilter(dim_x=7, dim_z=4) - - # State transition: constant velocity model - self.kalman_filter.F = np.array( - [ - [1, 0, 0, 0, 1, 0, 0], - [0, 1, 0, 0, 0, 1, 0], - [0, 0, 1, 0, 0, 0, 1], - [0, 0, 0, 1, 0, 0, 0], # aspect ratio: no velocity - [0, 0, 0, 0, 1, 0, 0], - [0, 0, 0, 0, 0, 1, 0], - [0, 0, 0, 0, 0, 0, 1], - ], - dtype=np.float64, - ) - - # Measurement function: observe (x, y, s, r) from state - self.kalman_filter.H = np.eye(4, 7, dtype=np.float64) - - # Noise tuning (from OC-SORT paper) - self.kalman_filter.R[2:, 2:] *= 10.0 - self.kalman_filter.P[4:, 4:] *= 1000.0 # high uncertainty for velocities - self.kalman_filter.P *= 10.0 - self.kalman_filter.Q[-1, -1] *= 0.01 - self.kalman_filter.Q[4:, 4:] *= 0.01 - - # Initialize state with first observation - self.kalman_filter.x[:4] = xyxy_to_xcycsr(initial_bbox).reshape((4, 1)) - - def _init_xyxy_filter(self, initial_bbox: np.ndarray) -> None: - """Initialize Kalman filter with XYXY state representation. - - State: [x1, y1, x2, y2, vx1, vy1, vx2, vy2] - Measurement: [x1, y1, x2, y2] - """ - self.kalman_filter = KalmanFilter(dim_x=8, dim_z=4) - - # State transition: constant velocity model for all coordinates - self.kalman_filter.F = np.array( - [ - [1, 0, 0, 0, 1, 0, 0, 0], # x1 += vx1 - [0, 1, 0, 0, 0, 1, 0, 0], # y1 += vy1 - [0, 0, 1, 0, 0, 0, 1, 0], # x2 += vx2 - [0, 0, 0, 1, 0, 0, 0, 1], # y2 += vy2 - [0, 0, 0, 0, 1, 0, 0, 0], # vx1 - [0, 0, 0, 0, 0, 1, 0, 0], # vy1 - [0, 0, 0, 0, 0, 0, 1, 0], # vx2 - [0, 0, 0, 0, 0, 0, 0, 1], # vy2 - ], - dtype=np.float64, - ) - - # Measurement function: observe (x1, y1, x2, y2) from state - self.kalman_filter.H = np.eye(4, 8, dtype=np.float64) - - # Noise tuning (similar scaling to XCYCSR version) - self.kalman_filter.R *= 1.0 # measurement noise - self.kalman_filter.P[4:, 4:] *= 1000.0 # high uncertainty for velocities - self.kalman_filter.P *= 10.0 - self.kalman_filter.Q[4:, 4:] *= 0.01 - - # Initialize state with first observation (direct XYXY) - self.kalman_filter.x[:4] = initial_bbox.reshape((4, 1)) - - def _bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: - """Convert bbox to measurement based on state representation.""" - if self.state_repr == StateRepresentation.XCYCSR: - return xyxy_to_xcycsr(bbox) - else: - return bbox - - def _state_to_bbox(self) -> np.ndarray: - """Convert current state to xyxy bbox.""" - if self.state_repr == StateRepresentation.XCYCSR: - return xcycsr_to_xyxy(self.kalman_filter.x[:4].reshape((4,))) - else: - return self.kalman_filter.x[:4].reshape((4,)) - @classmethod def get_next_tracker_id(cls) -> int: """Get next available tracker ID.""" @@ -213,8 +109,8 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: self.kalman_filter.set_state(self._frozen_state) time_gap = self.time_since_update - - if self.state_repr == StateRepresentation.XCYCSR: + # this is oc-sort specific + if isinstance(self.state_repr, XCYCSRStateRepresentation): self._unfreeze_xcycsr(new_bbox, time_gap) else: self._unfreeze_xyxy(new_bbox, time_gap) @@ -343,7 +239,7 @@ def update(self, bbox: np.ndarray | None) -> None: # Update KF with the real observation # (after ORU this is the final update at the correct time step; # without ORU this is the normal measurement update) - measurement = self._bbox_to_measurement(bbox) + measurement = self.state_repr.bbox_to_measurement(bbox) self.kalman_filter.update(measurement) self._observed = True @@ -366,9 +262,7 @@ def predict(self) -> np.ndarray: Predicted bounding box [x1, y1, x2, y2]. """ # If predicted scale would go negative, zero out scale velocity - if self.state_repr == StateRepresentation.XCYCSR: - if (self.kalman_filter.x[6] + self.kalman_filter.x[2]) <= 0: - self.kalman_filter.x[6] *= 0.0 + self.state_repr.clamp_velocity(self.kalman_filter) self.kalman_filter.predict() self.age += 1 @@ -377,7 +271,7 @@ def predict(self) -> np.ndarray: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 - return self._state_to_bbox() + return self.state_repr.state_to_bbox(self.kalman_filter) def is_lost(self) -> bool: """Check if tracklet is considered lost.""" @@ -389,4 +283,4 @@ def get_state_bbox(self) -> np.ndarray: Returns: Current bounding box estimate [x1, y1, x2, y2]. """ - return self._state_to_bbox() + return self.state_repr.state_to_bbox(self.kalman_filter) diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py new file mode 100644 index 00000000..1135f8c8 --- /dev/null +++ b/trackers/utils/state_representations.py @@ -0,0 +1,227 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +"""Kalman filter state representations for bounding box tracking. + +Provides pluggable state representations that define how bounding boxes are +encoded into a Kalman filter state vector. Each representation handles: +- Filter creation with appropriate dimensions and noise tuning +- Converting an ``[x1, y1, x2, y2]`` bbox to a measurement vector +- Converting the state vector back to an ``[x1, y1, x2, y2]`` bbox +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from enum import Enum + +import numpy as np + +from trackers.utils.converters import xcycsr_to_xyxy, xyxy_to_xcycsr +from trackers.utils.kalman_filter import KalmanFilter + + +class StateRepresentation(Enum): + """Kalman filter state representation for bounding boxes. + + XCYCSR: Center-based (x_center, y_center, scale, aspect_ratio, vx, vy, vs) + - 7 state variables, aspect ratio is constant (no velocity) + - Used in original SORT/OC-SORT papers + + XYXY: Corner-based (x1, y1, x2, y2, vx1, vy1, vx2, vy2) + - 8 state variables, all coordinates have velocities + - More direct representation, potentially better for non-rigid objects + """ + + XCYCSR = "xcycsr" + XYXY = "xyxy" + + +class BaseStateRepresentation(ABC): + """Abstract base for Kalman filter state representations. + + Subclasses define how bounding boxes map to/from a Kalman filter state + vector, and how the filter matrices (F, H, R, P, Q) are configured. + """ + + @abstractmethod + def create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: + """Create and initialise a Kalman filter for *initial_bbox*. + + Args: + initial_bbox: First detection ``[x1, y1, x2, y2]``. + + Returns: + A fully configured :class:`KalmanFilter`. + """ + + @abstractmethod + def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: + """Convert an ``[x1, y1, x2, y2]`` bbox to a measurement vector. + + Args: + bbox: Bounding box ``[x1, y1, x2, y2]``. + + Returns: + Measurement vector suitable for :meth:`KalmanFilter.update`. + """ + + @abstractmethod + def state_to_bbox(self, kf: KalmanFilter) -> np.ndarray: + """Extract an ``[x1, y1, x2, y2]`` bbox from the filter state. + + Args: + kf: The Kalman filter instance. + + Returns: + Bounding box ``[x1, y1, x2, y2]``. + """ + + @abstractmethod + def clamp_velocity(self, kf: KalmanFilter) -> None: + """Clamp velocity components to prevent degenerate predictions. + + Called before :meth:`KalmanFilter.predict` to ensure physical + plausibility (e.g. non-negative scale). + + Args: + kf: The Kalman filter instance (modified in-place). + """ + + +class XCYCSRStateRepresentation(BaseStateRepresentation): + """Center-based state: ``[x_c, y_c, scale, ratio, vx, vy, vs]``. + + 7 state dimensions, 4 measurement dimensions. + Aspect ratio is treated as constant (no velocity term). + Matches the representation used in the original SORT and OC-SORT papers. + """ + + def create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: + kf = KalmanFilter(dim_x=7, dim_z=4) + + # State transition: constant velocity model + kf.F = np.array( + [ + [1, 0, 0, 0, 1, 0, 0], + [0, 1, 0, 0, 0, 1, 0], + [0, 0, 1, 0, 0, 0, 1], + [0, 0, 0, 1, 0, 0, 0], # aspect ratio: no velocity + [0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 0, 1], + ], + dtype=np.float64, + ) + + # Measurement function: observe (x, y, s, r) from state + kf.H = np.eye(4, 7, dtype=np.float64) + + # Noise tuning (from OC-SORT paper) + kf.R[2:, 2:] *= 10.0 + kf.P[4:, 4:] *= 1000.0 # high uncertainty for velocities + kf.P *= 10.0 + kf.Q[-1, -1] *= 0.01 + kf.Q[4:, 4:] *= 0.01 + + # Initialise state with first observation + kf.x[:4] = xyxy_to_xcycsr(initial_bbox).reshape((4, 1)) + + return kf + + def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: + return xyxy_to_xcycsr(bbox) + + def state_to_bbox(self, kf: KalmanFilter) -> np.ndarray: + return xcycsr_to_xyxy(kf.x[:4].reshape((4,))) + + def clamp_velocity(self, kf: KalmanFilter) -> None: + # If predicted scale would go negative, zero out scale velocity + if (kf.x[6] + kf.x[2]) <= 0: + kf.x[6] *= 0.0 + + +class XYXYStateRepresentation(BaseStateRepresentation): + """Corner-based state: ``[x1, y1, x2, y2, vx1, vy1, vx2, vy2]``. + + 8 state dimensions, 4 measurement dimensions. + All four coordinates carry their own velocity term. + """ + + def create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: + kf = KalmanFilter(dim_x=8, dim_z=4) + + # State transition: constant velocity for all coordinates + kf.F = np.array( + [ + [1, 0, 0, 0, 1, 0, 0, 0], # x1 += vx1 + [0, 1, 0, 0, 0, 1, 0, 0], # y1 += vy1 + [0, 0, 1, 0, 0, 0, 1, 0], # x2 += vx2 + [0, 0, 0, 1, 0, 0, 0, 1], # y2 += vy2 + [0, 0, 0, 0, 1, 0, 0, 0], # vx1 + [0, 0, 0, 0, 0, 1, 0, 0], # vy1 + [0, 0, 0, 0, 0, 0, 1, 0], # vx2 + [0, 0, 0, 0, 0, 0, 0, 1], # vy2 + ], + dtype=np.float64, + ) + + # Measurement function: observe (x1, y1, x2, y2) from state + kf.H = np.eye(4, 8, dtype=np.float64) + + # Noise tuning (similar scaling to XCYCSR version) + kf.R *= 1.0 # measurement noise + kf.P[4:, 4:] *= 1000.0 # high uncertainty for velocities + kf.P *= 10.0 + kf.Q[4:, 4:] *= 0.01 + + # Initialise state with first observation (direct XYXY) + kf.x[:4] = initial_bbox.reshape((4, 1)) + + return kf + + def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: + return bbox + + def state_to_bbox(self, kf: KalmanFilter) -> np.ndarray: + return kf.x[:4].reshape((4,)) + + def clamp_velocity(self, kf: KalmanFilter) -> None: + # No clamping needed for XYXY representation + pass + + +# --------------------------------------------------------------------------- +# Factory helper +# --------------------------------------------------------------------------- + +_REPR_MAP: dict[str, type[BaseStateRepresentation]] = { + 'xcycsr': XCYCSRStateRepresentation, + 'xyxy': XYXYStateRepresentation, +} + + +def get_state_representation( + state_repr: str, +) -> BaseStateRepresentation: + """Return a :class:`BaseStateRepresentation` instance for *state_repr*. + + Args: + state_repr (str): The desired representation enum value. + + Returns: + An instance of the matching state representation class. + + Raises: + ValueError: If *state_repr* is not recognised. + """ + cls = _REPR_MAP.get(state_repr) + if cls is None: + raise ValueError( + f"Unknown state representation: {state_repr!r}. " + f"Available: {list(_REPR_MAP.keys())}" + ) + return cls() From 4b9bfbaeb5a0d0c8267fe5d0c126307d0a9085a1 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Mon, 23 Feb 2026 11:46:17 -0300 Subject: [PATCH 45/87] changed state representations to KF classes --- trackers/core/ocsort/tracker.py | 11 ++- trackers/core/ocsort/tracklet.py | 43 ++++---- trackers/utils/state_representations.py | 124 ++++++++++++++++-------- 3 files changed, 113 insertions(+), 65 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 297a084e..a850d65c 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -18,7 +18,7 @@ get_iou_matrix, get_iou_matrix_between_boxes, ) -from trackers.utils.state_representations import XCYCSRStateRepresentation +from trackers.utils.state_representations import XCYCSRKalmanFilter class OCSORTTracker(BaseTracker): @@ -80,10 +80,11 @@ def __init__( self.direction_consistency_weight = direction_consistency_weight self.high_conf_det_threshold = high_conf_det_threshold self.delta_t = delta_t - + self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 - self.state_representation = XCYCSRStateRepresentation() + self.kalman_filter_class = XCYCSRKalmanFilter + def _get_associated_indices( self, iou_matrix: np.ndarray, @@ -143,7 +144,9 @@ def _spawn_new_tracklets( """ for detection_idx in unmatched_detections: new_tracker = OCSORTTracklet( - detections.xyxy[detection_idx], delta_t=self.delta_t, state_representation=self.state_representation + detections.xyxy[detection_idx], + delta_t=self.delta_t, + kalman_filter_class=self.kalman_filter_class, ) self.tracks.append(new_tracker) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 2e98fb72..0cc2abc9 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -9,11 +9,13 @@ import numpy as np from trackers.utils.converters import ( - xcycsr_to_xyxy, xyxy_to_xcycsr, ) -from trackers.utils.kalman_filter import KalmanFilter -from trackers.utils.state_representations import BaseStateRepresentation, XCYCSRStateRepresentation +from trackers.utils.state_representations import ( + BaseKalmanFilter, + XCYCSRKalmanFilter, +) + class OCSORTTracklet: """Tracklet for OC-SORT tracker with ORU (Observation-centric Re-Update). @@ -26,7 +28,7 @@ class OCSORTTracklet: Attributes: age: Age of the tracklet in frames. - kalman_filter: The Kalman filter instance for state estimation. + kalman_filter: The Kalman filter wrapping the state representation. tracker_id: Unique identifier (-1 until track is mature). number_of_successful_consecutive_updates: Consecutive successful updates. time_since_update: Frames since last observation. @@ -35,7 +37,6 @@ class OCSORTTracklet: observations: Dict mapping age to observed bbox for delta_t lookback. velocity: Normalized direction vector computed with delta_t lookback. delta_t: Number of timesteps back to look for velocity estimation. - state_repr: The state representation being used. """ count_id: int = 0 @@ -43,24 +44,24 @@ class OCSORTTracklet: def __init__( self, initial_bbox: np.ndarray, - state_repr: BaseStateRepresentation = XCYCSRStateRepresentation, + kalman_filter_class: type[BaseKalmanFilter] = XCYCSRKalmanFilter, delta_t: int = 3, ) -> None: """Initialize tracklet with first detection. Args: initial_bbox: Initial bounding box [x1, y1, x2, y2]. - state_repr: State representation to use (XCYCSR or XYXY). + kalman_filter_class: Kalman filter class to use. Instantiated + with *initial_bbox*. Defaults to + :class:`XCYCSRKalmanFilter`. delta_t: Number of timesteps back to look for velocity estimation. Higher values use observations further in the past to estimate motion direction, providing more stable velocity estimates. """ self.age = 0 - self.state_repr = state_repr - - # Initialize Kalman filter based on state representation - self.kalman_filter = self.state_repr.create_filter(initial_bbox) + # Initialize Kalman filter (wraps raw KalmanFilter + state repr) + self.kalman_filter: BaseKalmanFilter = kalman_filter_class(initial_bbox) # Observation history for ORU and delta_t self.delta_t = delta_t @@ -110,7 +111,7 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: time_gap = self.time_since_update # this is oc-sort specific - if isinstance(self.state_repr, XCYCSRStateRepresentation): + if isinstance(self.kalman_filter, XCYCSRKalmanFilter): self._unfreeze_xcycsr(new_bbox, time_gap) else: self._unfreeze_xyxy(new_bbox, time_gap) @@ -155,9 +156,9 @@ def _unfreeze_xcycsr(self, new_bbox: np.ndarray, time_gap: int) -> None: r = w / h virtual_obs = np.array([x, y, s, r]).reshape((4, 1)) - self.kalman_filter.update(virtual_obs) + self.kalman_filter.kf.update(virtual_obs) if i < time_gap - 1: - self.kalman_filter.predict() + self.kalman_filter.kf.predict() def _unfreeze_xyxy(self, new_bbox: np.ndarray, time_gap: int) -> None: """ORU interpolation for XYXY representation. @@ -174,9 +175,9 @@ def _unfreeze_xyxy(self, new_bbox: np.ndarray, time_gap: int) -> None: for i in range(time_gap): virtual_obs = (last_xyxy + (i + 1) * delta).reshape((4, 1)) - self.kalman_filter.update(virtual_obs) + self.kalman_filter.kf.update(virtual_obs) if i < time_gap - 1: - self.kalman_filter.predict() + self.kalman_filter.kf.predict() def get_k_previous_obs(self) -> np.ndarray | None: """Get observation from delta_t steps ago. @@ -239,8 +240,7 @@ def update(self, bbox: np.ndarray | None) -> None: # Update KF with the real observation # (after ORU this is the final update at the correct time step; # without ORU this is the normal measurement update) - measurement = self.state_repr.bbox_to_measurement(bbox) - self.kalman_filter.update(measurement) + self.kalman_filter.update(bbox) self._observed = True self.time_since_update = 0 @@ -261,9 +261,6 @@ def predict(self) -> np.ndarray: Returns: Predicted bounding box [x1, y1, x2, y2]. """ - # If predicted scale would go negative, zero out scale velocity - self.state_repr.clamp_velocity(self.kalman_filter) - self.kalman_filter.predict() self.age += 1 @@ -271,7 +268,7 @@ def predict(self) -> np.ndarray: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 - return self.state_repr.state_to_bbox(self.kalman_filter) + return self.kalman_filter.state_to_bbox() def is_lost(self) -> bool: """Check if tracklet is considered lost.""" @@ -283,4 +280,4 @@ def get_state_bbox(self) -> np.ndarray: Returns: Current bounding box estimate [x1, y1, x2, y2]. """ - return self.state_repr.state_to_bbox(self.kalman_filter) + return self.kalman_filter.state_to_bbox() diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py index 1135f8c8..22842e05 100644 --- a/trackers/utils/state_representations.py +++ b/trackers/utils/state_representations.py @@ -40,16 +40,30 @@ class StateRepresentation(Enum): XYXY = "xyxy" -class BaseStateRepresentation(ABC): - """Abstract base for Kalman filter state representations. +class BaseKalmanFilter(ABC): + """Abstract Kalman filter with a specific bounding box state representation. - Subclasses define how bounding boxes map to/from a Kalman filter state - vector, and how the filter matrices (F, H, R, P, Q) are configured. + Wraps a :class:`KalmanFilter` and provides a unified interface for + bounding-box tracking regardless of the internal state encoding. + Subclasses configure the filter dimensions, matrices, noise, and + handle conversions between ``[x1, y1, x2, y2]`` bboxes and the + internal state/measurement vectors. + + Attributes: + kf: The underlying Kalman filter instance. """ + def __init__(self, initial_bbox: np.ndarray) -> None: + """Initialise the filter with the first detection. + + Args: + initial_bbox: First detection ``[x1, y1, x2, y2]``. + """ + self.kf: KalmanFilter = self._create_filter(initial_bbox) + @abstractmethod - def create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: - """Create and initialise a Kalman filter for *initial_bbox*. + def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: + """Create and configure a Kalman filter for *initial_bbox*. Args: initial_bbox: First detection ``[x1, y1, x2, y2]``. @@ -70,37 +84,68 @@ def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: """ @abstractmethod - def state_to_bbox(self, kf: KalmanFilter) -> np.ndarray: - """Extract an ``[x1, y1, x2, y2]`` bbox from the filter state. - - Args: - kf: The Kalman filter instance. + def state_to_bbox(self) -> np.ndarray: + """Extract an ``[x1, y1, x2, y2]`` bbox from the current filter state. Returns: Bounding box ``[x1, y1, x2, y2]``. """ @abstractmethod - def clamp_velocity(self, kf: KalmanFilter) -> None: + def clamp_velocity(self) -> None: """Clamp velocity components to prevent degenerate predictions. - Called before :meth:`KalmanFilter.predict` to ensure physical - plausibility (e.g. non-negative scale). + Called before :meth:`predict` to ensure physical plausibility + (e.g. non-negative scale). Modifies the filter state in-place. + """ + + # ------------------------------------------------------------------ + # Delegated KalmanFilter operations + # ------------------------------------------------------------------ + + def predict(self) -> None: + """Run the Kalman filter prediction step.""" + self.clamp_velocity() + self.kf.predict() + + def update(self, bbox: np.ndarray | None) -> None: + """Update the filter with a new observation. + + Args: + bbox: Bounding box ``[x1, y1, x2, y2]`` or ``None`` when no + observation is available. + """ + if bbox is not None: + self.kf.update(self.bbox_to_measurement(bbox)) + else: + self.kf.update(None) + + def get_state(self) -> dict: + """Snapshot the filter state for later restoration (e.g. ORU freeze). + + Returns: + Opaque state dictionary. + """ + return self.kf.get_state() + + def set_state(self, state: dict) -> None: + """Restore a previously saved filter state. Args: - kf: The Kalman filter instance (modified in-place). + state: Dictionary from :meth:`get_state`. """ + self.kf.set_state(state) -class XCYCSRStateRepresentation(BaseStateRepresentation): - """Center-based state: ``[x_c, y_c, scale, ratio, vx, vy, vs]``. +class XCYCSRKalmanFilter(BaseKalmanFilter): + """Center-based Kalman filter: ``[x_c, y_c, scale, ratio, vx, vy, vs]``. 7 state dimensions, 4 measurement dimensions. Aspect ratio is treated as constant (no velocity term). Matches the representation used in the original SORT and OC-SORT papers. """ - def create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: + def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: kf = KalmanFilter(dim_x=7, dim_z=4) # State transition: constant velocity model @@ -135,23 +180,23 @@ def create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: return xyxy_to_xcycsr(bbox) - def state_to_bbox(self, kf: KalmanFilter) -> np.ndarray: - return xcycsr_to_xyxy(kf.x[:4].reshape((4,))) + def state_to_bbox(self) -> np.ndarray: + return xcycsr_to_xyxy(self.kf.x[:4].reshape((4,))) - def clamp_velocity(self, kf: KalmanFilter) -> None: + def clamp_velocity(self) -> None: # If predicted scale would go negative, zero out scale velocity - if (kf.x[6] + kf.x[2]) <= 0: - kf.x[6] *= 0.0 + if (self.kf.x[6] + self.kf.x[2]) <= 0: + self.kf.x[6] *= 0.0 -class XYXYStateRepresentation(BaseStateRepresentation): - """Corner-based state: ``[x1, y1, x2, y2, vx1, vy1, vx2, vy2]``. +class XYXYKalmanFilter(BaseKalmanFilter): + """Corner-based Kalman filter: ``[x1, y1, x2, y2, vx1, vy1, vx2, vy2]``. 8 state dimensions, 4 measurement dimensions. All four coordinates carry their own velocity term. """ - def create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: + def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: kf = KalmanFilter(dim_x=8, dim_z=4) # State transition: constant velocity for all coordinates @@ -186,10 +231,10 @@ def create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: return bbox - def state_to_bbox(self, kf: KalmanFilter) -> np.ndarray: - return kf.x[:4].reshape((4,)) + def state_to_bbox(self) -> np.ndarray: + return self.kf.x[:4].reshape((4,)) - def clamp_velocity(self, kf: KalmanFilter) -> None: + def clamp_velocity(self) -> None: # No clamping needed for XYXY representation pass @@ -198,22 +243,25 @@ def clamp_velocity(self, kf: KalmanFilter) -> None: # Factory helper # --------------------------------------------------------------------------- -_REPR_MAP: dict[str, type[BaseStateRepresentation]] = { - 'xcycsr': XCYCSRStateRepresentation, - 'xyxy': XYXYStateRepresentation, +_REPR_MAP: dict[str, type[BaseKalmanFilter]] = { + "xcycsr": XCYCSRKalmanFilter, + "xyxy": XYXYKalmanFilter, } -def get_state_representation( +def create_kalman_filter( state_repr: str, -) -> BaseStateRepresentation: - """Return a :class:`BaseStateRepresentation` instance for *state_repr*. + initial_bbox: np.ndarray, +) -> BaseKalmanFilter: + """Create a Kalman filter for the given state representation. Args: - state_repr (str): The desired representation enum value. + state_repr: The desired representation (``"xcycsr"`` or ``"xyxy"``). + initial_bbox: First detection ``[x1, y1, x2, y2]``. Returns: - An instance of the matching state representation class. + An initialised :class:`BaseKalmanFilter` wrapping a configured + Kalman filter. Raises: ValueError: If *state_repr* is not recognised. @@ -224,4 +272,4 @@ def get_state_representation( f"Unknown state representation: {state_repr!r}. " f"Available: {list(_REPR_MAP.keys())}" ) - return cls() + return cls(initial_bbox) From c9ef80bbac58684e3165b58b2d4f61bccdc57939 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 23 Feb 2026 14:46:37 +0000 Subject: [PATCH 46/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- trackers/core/ocsort/__init__.py | 5 +++++ trackers/utils/converters.py | 6 ++++++ trackers/utils/kalman_filter.py | 6 ++++++ 3 files changed, 17 insertions(+) diff --git a/trackers/core/ocsort/__init__.py b/trackers/core/ocsort/__init__.py index e69de29b..57226e88 100644 --- a/trackers/core/ocsort/__init__.py +++ b/trackers/core/ocsort/__init__.py @@ -0,0 +1,5 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ diff --git a/trackers/utils/converters.py b/trackers/utils/converters.py index 798ce229..9251233c 100644 --- a/trackers/utils/converters.py +++ b/trackers/utils/converters.py @@ -1,3 +1,9 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + # ------------------------------------------------------------------------ # Trackers # Copyright (c) 2026 Roboflow. All Rights Reserved. diff --git a/trackers/utils/kalman_filter.py b/trackers/utils/kalman_filter.py index 1579933a..aee3eb5e 100644 --- a/trackers/utils/kalman_filter.py +++ b/trackers/utils/kalman_filter.py @@ -1,3 +1,9 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + # ------------------------------------------------------------------------ # Trackers # Copyright (c) 2026 Roboflow. All Rights Reserved. From 03b6c0e40776648ce5b90e4116b6dc6f2d7a0c16 Mon Sep 17 00:00:00 2001 From: SkalskiP Date: Mon, 23 Feb 2026 22:55:27 +0100 Subject: [PATCH 47/87] register OCSORTTracker for CLI and public API - Add `tracker_id = "ocsort"` class variable to enable CLI auto-registration via BaseTracker's registry mechanism - Export OCSORTTracker in `__all__` for public API discoverability --- trackers/__init__.py | 1 + trackers/core/ocsort/tracker.py | 14 ++++++++------ 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/trackers/__init__.py b/trackers/__init__.py index 3e9fddcd..b3180985 100644 --- a/trackers/__init__.py +++ b/trackers/__init__.py @@ -23,6 +23,7 @@ "IdentityTransformation", "MotionAwareTraceAnnotator", "MotionEstimator", + "OCSORTTracker", "SORTTracker", "frames_from_source", ] diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index a850d65c..b120bd2a 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -48,18 +48,20 @@ class OCSORTTracker(BaseTracker): `-1` as its `tracker_id`. minimum_iou_threshold: IOU threshold for associating detections to existing tracks. - direction_consistency_weight: Weight for inertia term in association cost. Higher values give more importance - to the angle difference between the motion direction and the association direction. - high_conf_det_threshold: Confidence threshold to consider a detection as high confidence. If a detection has - confidence lower than this threshold, it will not be considered for association. + direction_consistency_weight: Weight for inertia term in association cost. + Higher values give more importance to the angle difference between the + motion direction and the association direction. + high_conf_det_threshold: Confidence threshold to consider a detection as high + confidence. If a detection has confidence lower than this threshold, it + will not be considered for association. delta_t: Number of timesteps back to look for velocity/direction estimation. Higher values use observations further in the past to compute motion direction, providing more stable velocity estimates during occlusion. Default is 3 (matching the original OC-SORT paper). - """ # noqa: E501 + """ - count_id: int = 0 + tracker_id = "ocsort" def __init__( self, From 7b66fe916e808250231e98581eb5cf8b3e321a7a Mon Sep 17 00:00:00 2001 From: SkalskiP Date: Mon, 23 Feb 2026 23:24:06 +0100 Subject: [PATCH 48/87] remove duplicate headers and forbidden comments in utils - Remove module-level docstring from state_representations.py - Remove section divider comment from state_representations.py - Remove duplicate/truncated headers from converters.py and kalman_filter.py --- trackers/utils/converters.py | 6 ------ trackers/utils/kalman_filter.py | 6 ------ trackers/utils/state_representations.py | 13 ------------- 3 files changed, 25 deletions(-) diff --git a/trackers/utils/converters.py b/trackers/utils/converters.py index 9251233c..bb28bfa3 100644 --- a/trackers/utils/converters.py +++ b/trackers/utils/converters.py @@ -4,12 +4,6 @@ # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ -# ------------------------------------------------------------------------ -# Trackers -# Copyright (c) 2026 Roboflow. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------- - import numpy as np diff --git a/trackers/utils/kalman_filter.py b/trackers/utils/kalman_filter.py index aee3eb5e..a12233f2 100644 --- a/trackers/utils/kalman_filter.py +++ b/trackers/utils/kalman_filter.py @@ -4,12 +4,6 @@ # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ -# ------------------------------------------------------------------------ -# Trackers -# Copyright (c) 2026 Roboflow. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------- - from __future__ import annotations import numpy as np diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py index 22842e05..cae9ef38 100644 --- a/trackers/utils/state_representations.py +++ b/trackers/utils/state_representations.py @@ -4,15 +4,6 @@ # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ -"""Kalman filter state representations for bounding box tracking. - -Provides pluggable state representations that define how bounding boxes are -encoded into a Kalman filter state vector. Each representation handles: -- Filter creation with appropriate dimensions and noise tuning -- Converting an ``[x1, y1, x2, y2]`` bbox to a measurement vector -- Converting the state vector back to an ``[x1, y1, x2, y2]`` bbox -""" - from __future__ import annotations from abc import ABC, abstractmethod @@ -99,10 +90,6 @@ def clamp_velocity(self) -> None: (e.g. non-negative scale). Modifies the filter state in-place. """ - # ------------------------------------------------------------------ - # Delegated KalmanFilter operations - # ------------------------------------------------------------------ - def predict(self) -> None: """Run the Kalman filter prediction step.""" self.clamp_velocity() From 765f1bafa9c83b483d452d8f881f77b87308d890 Mon Sep 17 00:00:00 2001 From: SkalskiP Date: Mon, 23 Feb 2026 23:46:30 +0100 Subject: [PATCH 49/87] Use mkdocstrings-compatible single backticks instead of Sphinx-style double backticks in state_representations.py. --- trackers/utils/state_representations.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py index cae9ef38..f04e5f3b 100644 --- a/trackers/utils/state_representations.py +++ b/trackers/utils/state_representations.py @@ -37,7 +37,7 @@ class BaseKalmanFilter(ABC): Wraps a :class:`KalmanFilter` and provides a unified interface for bounding-box tracking regardless of the internal state encoding. Subclasses configure the filter dimensions, matrices, noise, and - handle conversions between ``[x1, y1, x2, y2]`` bboxes and the + handle conversions between `[x1, y1, x2, y2]` bboxes and the internal state/measurement vectors. Attributes: @@ -48,7 +48,7 @@ def __init__(self, initial_bbox: np.ndarray) -> None: """Initialise the filter with the first detection. Args: - initial_bbox: First detection ``[x1, y1, x2, y2]``. + initial_bbox: First detection `[x1, y1, x2, y2]`. """ self.kf: KalmanFilter = self._create_filter(initial_bbox) @@ -57,7 +57,7 @@ def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: """Create and configure a Kalman filter for *initial_bbox*. Args: - initial_bbox: First detection ``[x1, y1, x2, y2]``. + initial_bbox: First detection `[x1, y1, x2, y2]`. Returns: A fully configured :class:`KalmanFilter`. @@ -65,10 +65,10 @@ def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: @abstractmethod def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: - """Convert an ``[x1, y1, x2, y2]`` bbox to a measurement vector. + """Convert an `[x1, y1, x2, y2]` bbox to a measurement vector. Args: - bbox: Bounding box ``[x1, y1, x2, y2]``. + bbox: Bounding box `[x1, y1, x2, y2]`. Returns: Measurement vector suitable for :meth:`KalmanFilter.update`. @@ -76,10 +76,10 @@ def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: @abstractmethod def state_to_bbox(self) -> np.ndarray: - """Extract an ``[x1, y1, x2, y2]`` bbox from the current filter state. + """Extract an `[x1, y1, x2, y2]` bbox from the current filter state. Returns: - Bounding box ``[x1, y1, x2, y2]``. + Bounding box `[x1, y1, x2, y2]`. """ @abstractmethod @@ -99,7 +99,7 @@ def update(self, bbox: np.ndarray | None) -> None: """Update the filter with a new observation. Args: - bbox: Bounding box ``[x1, y1, x2, y2]`` or ``None`` when no + bbox: Bounding box `[x1, y1, x2, y2]` or `None` when no observation is available. """ if bbox is not None: @@ -125,7 +125,7 @@ def set_state(self, state: dict) -> None: class XCYCSRKalmanFilter(BaseKalmanFilter): - """Center-based Kalman filter: ``[x_c, y_c, scale, ratio, vx, vy, vs]``. + """Center-based Kalman filter: `[x_c, y_c, scale, ratio, vx, vy, vs]`. 7 state dimensions, 4 measurement dimensions. Aspect ratio is treated as constant (no velocity term). @@ -177,7 +177,7 @@ def clamp_velocity(self) -> None: class XYXYKalmanFilter(BaseKalmanFilter): - """Corner-based Kalman filter: ``[x1, y1, x2, y2, vx1, vy1, vx2, vy2]``. + """Corner-based Kalman filter: `[x1, y1, x2, y2, vx1, vy1, vx2, vy2]`. 8 state dimensions, 4 measurement dimensions. All four coordinates carry their own velocity term. @@ -243,8 +243,8 @@ def create_kalman_filter( """Create a Kalman filter for the given state representation. Args: - state_repr: The desired representation (``"xcycsr"`` or ``"xyxy"``). - initial_bbox: First detection ``[x1, y1, x2, y2]``. + state_repr: The desired representation (`"xcycsr"` or `"xyxy"`). + initial_bbox: First detection `[x1, y1, x2, y2]`. Returns: An initialised :class:`BaseKalmanFilter` wrapping a configured From 31b5be1cd18b83996c577d2e58318b9dfaec008d Mon Sep 17 00:00:00 2001 From: SkalskiP Date: Tue, 24 Feb 2026 08:33:57 +0100 Subject: [PATCH 50/87] wrap OCSORTTracker docstring to fit 88-char line limit --- trackers/core/ocsort/tracker.py | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index b120bd2a..d608d066 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -24,15 +24,19 @@ class OCSORTTracker(BaseTracker): """Implements OC-SORT (Observation Centric Simple Online and Realtime Tracking). - OC-SORT remains Simple, Online, and Real-Time but improves robustness during occlusion and non-linear motion. - It recognizes limitations from SORT and the linear motion assumption of the Kalman filter, and adds three - mechanisms to enhance tracking. The first mechanism is Observation-Centre Re-Update (ORU), which runs a - predict-update loop with a 'virtual trajectory' in order to have less noisy Kalman Filter parameters once - a track is re-activated after being lost. The second mechanism is Observation-Centric Momentum (OCM), that - incorporates the direction consistency of tracks in the cost matrix for the association. Finally, OC-SORT adds - Observation-centric Recovery (OCR), a second-stage association step between the last observation of unmatched - tracks to the unmatched observations after the usual association. It attempts to recover tracks that were lost - due to object stopping or short-term occlusion. + OC-SORT remains Simple, Online, and Real-Time but improves robustness during + occlusion and non-linear motion. It recognizes limitations from SORT and the + linear motion assumption of the Kalman filter, and adds three mechanisms to + enhance tracking. The first mechanism is Observation-Centre Re-Update (ORU), + which runs a predict-update loop with a 'virtual trajectory' in order to have + less noisy Kalman Filter parameters once a track is re-activated after being + lost. The second mechanism is Observation-Centric Momentum (OCM), that + incorporates the direction consistency of tracks in the cost matrix for the + association. Finally, OC-SORT adds Observation-centric Recovery (OCR), a + second-stage association step between the last observation of unmatched tracks + to the unmatched observations after the usual association. It attempts to + recover tracks that were lost due to object stopping or short-term occlusion. + Args: lost_track_buffer: Number of frames to buffer when a track is lost. Increasing lost_track_buffer enhances occlusion handling, significantly From 39c7fd3c1edc88647601900483975e7179c4294f Mon Sep 17 00:00:00 2001 From: SkalskiP Date: Tue, 24 Feb 2026 09:41:12 +0100 Subject: [PATCH 51/87] docs: improve docstrings for mkdocs compatibility and clarity - Add backticks to variable references (`x1`, `y1`, `scale`, etc.) - Replace Sphinx-style :class:/:meth: with plain backticks - Remove bullet points (poor mkdocs rendering) - Expand StateRepresentation, XCYCSRKalmanFilter, XYXYKalmanFilter docstrings with variable explanations and behavioral implications --- trackers/core/ocsort/tracker.py | 2 +- trackers/core/ocsort/tracklet.py | 25 +++++++------ trackers/core/ocsort/utils.py | 28 +++++++-------- trackers/utils/state_representations.py | 47 ++++++++++++++----------- 4 files changed, 54 insertions(+), 48 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index d608d066..1647d13a 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -146,7 +146,7 @@ def _spawn_new_tracklets( Args: detections: The latest set of object detections. detection_boxes: Detected bounding boxes in the - form [x1, y1, x2, y2]. + form `[x1, y1, x2, y2]`. """ for detection_idx in unmatched_detections: new_tracker = OCSORTTracklet( diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 0cc2abc9..557919ff 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -21,10 +21,9 @@ class OCSORTTracklet: """Tracklet for OC-SORT tracker with ORU (Observation-centric Re-Update). Manages a single tracked object with Kalman filter state estimation. - Implements OC-SORT specific features: - - Freeze/unfreeze for saving state before track is lost - - Virtual trajectory generation (ORU) for recovering lost tracks - - Configurable state representation (XCYCSR or XYXY) + Implements OC-SORT specific features: freeze/unfreeze for saving state + before track is lost, virtual trajectory generation (ORU) for recovering + lost tracks, and configurable state representation (XCYCSR or XYXY). Attributes: age: Age of the tracklet in frames. @@ -32,7 +31,7 @@ class OCSORTTracklet: tracker_id: Unique identifier (-1 until track is mature). number_of_successful_consecutive_updates: Consecutive successful updates. time_since_update: Frames since last observation. - last_observation: Last observed bounding box [x1, y1, x2, y2]. + last_observation: Last observed bounding box `[x1, y1, x2, y2]`. previous_to_last_observation: Second-to-last observation for velocity. observations: Dict mapping age to observed bbox for delta_t lookback. velocity: Normalized direction vector computed with delta_t lookback. @@ -50,10 +49,10 @@ def __init__( """Initialize tracklet with first detection. Args: - initial_bbox: Initial bounding box [x1, y1, x2, y2]. + initial_bbox: Initial bounding box `[x1, y1, x2, y2]`. kalman_filter_class: Kalman filter class to use. Instantiated with *initial_bbox*. Defaults to - :class:`XCYCSRKalmanFilter`. + `XCYCSRKalmanFilter`. delta_t: Number of timesteps back to look for velocity estimation. Higher values use observations further in the past to estimate motion direction, providing more stable velocity estimates. @@ -101,7 +100,7 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: trajectory. Args: - new_bbox: New observation bounding box [x1, y1, x2, y2]. + new_bbox: New observation bounding box `[x1, y1, x2, y2]`. """ if self._frozen_state is None: return @@ -203,8 +202,8 @@ def _compute_velocity(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: """Compute normalized direction vector between two bounding box centers. Args: - bbox1: First bounding box [x1, y1, x2, y2]. - bbox2: Second bounding box [x1, y1, x2, y2]. + bbox1: First bounding box `[x1, y1, x2, y2]`. + bbox2: Second bounding box `[x1, y1, x2, y2]`. Returns: Normalized direction vector [dy, dx]. @@ -223,7 +222,7 @@ def update(self, bbox: np.ndarray | None) -> None: Computes velocity using observation from delta_t steps ago. Args: - bbox: Bounding box [x1, y1, x2, y2] or None for no observation. + bbox: Bounding box `[x1, y1, x2, y2]` or None for no observation. """ if bbox is not None: # Compute velocity only after the track has been observed at least once @@ -259,7 +258,7 @@ def predict(self) -> np.ndarray: """Predict next bounding box position. Returns: - Predicted bounding box [x1, y1, x2, y2]. + Predicted bounding box `[x1, y1, x2, y2]`. """ self.kalman_filter.predict() self.age += 1 @@ -278,6 +277,6 @@ def get_state_bbox(self) -> np.ndarray: """Get current bounding box estimate from Kalman filter. Returns: - Current bounding box estimate [x1, y1, x2, y2]. + Current bounding box estimate `[x1, y1, x2, y2]`. """ return self.kalman_filter.state_to_bbox() diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 043d4c00..8f00fdc5 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -50,8 +50,8 @@ def _speed_direction(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: """Compute normalized direction vector between two bounding box centers. Args: - bbox1: First bounding box in the form [x1, y1, x2, y2]. - bbox2: Second bounding box in the form [x1, y1, x2, y2]. + bbox1: First bounding box in the form `[x1, y1, x2, y2]`. + bbox2: Second bounding box in the form `[x1, y1, x2, y2]`. Returns: np.ndarray: Normalized direction vector [dy, dx] from bbox1 to bbox2. @@ -79,7 +79,7 @@ def _build_direction_consistency_matrix( Args: tracklets: List of OCSORTTracklet objects. - detection_boxes: Detection bounding boxes [x1, y1, x2, y2]. + detection_boxes: Detection bounding boxes `[x1, y1, x2, y2]`. Returns: np.ndarray: Direction consistency cost matrix (n_tracklets, n_detections). @@ -118,8 +118,8 @@ def _speed_direction_batch( """Compute normalized direction vectors from tracks to detections in batch. Args: - dets: Detection bounding boxes [x1, y1, x2, y2], shape (n_dets, 4). - tracks: Track bounding boxes [x1, y1, x2, y2], shape (n_tracks, 4). + dets: Detection bounding boxes `[x1, y1, x2, y2]`, shape (n_dets, 4). + tracks: Track bounding boxes `[x1, y1, x2, y2]`, shape (n_tracks, 4). Returns: tuple[np.ndarray, np.ndarray]: (dy, dx) direction vectors, @@ -148,7 +148,7 @@ def build_direction_consistency_matrix_batch( Args: tracklets: List of OCSORTTracklet objects. - detection_boxes: Detection bounding boxes [x1, y1, x2, y2]. + detection_boxes: Detection bounding boxes `[x1, y1, x2, y2]`. Returns: np.ndarray: Direction consistency cost matrix (n_tracklets, n_detections). @@ -212,11 +212,11 @@ def add_track_id_detections( ) -> None: """Assign track ID to detection and add to updated_detections list. - Handles ID assignment based on track maturity: - - Early frames (frame_count < minimum_consecutive_frames): Assign ID if - track was just updated and doesn't have an ID yet. - - Later frames: Assign ID only if track is mature (has enough consecutive - updates). Immature tracks get tracker_id = -1. + Handles ID assignment based on track maturity. In early frames + (`frame_count < minimum_consecutive_frames`), assigns ID if track was just + updated and doesn't have an ID yet. In later frames, assigns ID only if + track is mature (has enough consecutive updates). Immature tracks get + `tracker_id = -1`. Args: track: The tracklet being processed. @@ -254,7 +254,7 @@ def get_iou_matrix( Args: trackers: Sequence of OCSORTTracklet objects. - detection_boxes: Detection bounding boxes [x1, y1, x2, y2]. + detection_boxes: Detection bounding boxes `[x1, y1, x2, y2]`. Returns: np.ndarray: IOU matrix of shape (n_trackers, n_detections). @@ -277,8 +277,8 @@ def get_iou_matrix_between_boxes( """Build IOU cost matrix between two sets of bounding boxes. Args: - last_observations: First set of boxes [x1, y1, x2, y2]. - detection_boxes: Second set of boxes [x1, y1, x2, y2]. + last_observations: First set of boxes `[x1, y1, x2, y2]`. + detection_boxes: Second set of boxes `[x1, y1, x2, y2]`. Returns: np.ndarray: IOU matrix of shape (n_observations, n_detections). diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py index f04e5f3b..ac7de038 100644 --- a/trackers/utils/state_representations.py +++ b/trackers/utils/state_representations.py @@ -18,13 +18,16 @@ class StateRepresentation(Enum): """Kalman filter state representation for bounding boxes. - XCYCSR: Center-based (x_center, y_center, scale, aspect_ratio, vx, vy, vs) - - 7 state variables, aspect ratio is constant (no velocity) - - Used in original SORT/OC-SORT papers - - XYXY: Corner-based (x1, y1, x2, y2, vx1, vy1, vx2, vy2) - - 8 state variables, all coordinates have velocities - - More direct representation, potentially better for non-rigid objects + Attributes: + XCYCSR: Center-based representation with 7 state variables: + `x_center`, `y_center` (box center), `scale` (area), `aspect_ratio` + (width/height), and velocities `vx`, `vy`, `vs`. Aspect ratio is + treated as constant (no velocity term). Used in original SORT and + OC-SORT papers. + XYXY: Corner-based representation with 8 state variables: + `x1`, `y1` (top-left corner), `x2`, `y2` (bottom-right corner), + and velocities `vx1`, `vy1`, `vx2`, `vy2` for each coordinate. + More direct representation, potentially better for non-rigid objects. """ XCYCSR = "xcycsr" @@ -34,7 +37,7 @@ class StateRepresentation(Enum): class BaseKalmanFilter(ABC): """Abstract Kalman filter with a specific bounding box state representation. - Wraps a :class:`KalmanFilter` and provides a unified interface for + Wraps a `KalmanFilter` and provides a unified interface for bounding-box tracking regardless of the internal state encoding. Subclasses configure the filter dimensions, matrices, noise, and handle conversions between `[x1, y1, x2, y2]` bboxes and the @@ -60,7 +63,7 @@ def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: initial_bbox: First detection `[x1, y1, x2, y2]`. Returns: - A fully configured :class:`KalmanFilter`. + A fully configured `KalmanFilter`. """ @abstractmethod @@ -71,7 +74,7 @@ def bbox_to_measurement(self, bbox: np.ndarray) -> np.ndarray: bbox: Bounding box `[x1, y1, x2, y2]`. Returns: - Measurement vector suitable for :meth:`KalmanFilter.update`. + Measurement vector suitable for `KalmanFilter.update`. """ @abstractmethod @@ -86,7 +89,7 @@ def state_to_bbox(self) -> np.ndarray: def clamp_velocity(self) -> None: """Clamp velocity components to prevent degenerate predictions. - Called before :meth:`predict` to ensure physical plausibility + Called before `predict` to ensure physical plausibility (e.g. non-negative scale). Modifies the filter state in-place. """ @@ -119,17 +122,19 @@ def set_state(self, state: dict) -> None: """Restore a previously saved filter state. Args: - state: Dictionary from :meth:`get_state`. + state: Dictionary from `get_state`. """ self.kf.set_state(state) class XCYCSRKalmanFilter(BaseKalmanFilter): - """Center-based Kalman filter: `[x_c, y_c, scale, ratio, vx, vy, vs]`. + """Center-based Kalman filter with 7 state dimensions and 4 measurements. - 7 state dimensions, 4 measurement dimensions. - Aspect ratio is treated as constant (no velocity term). - Matches the representation used in the original SORT and OC-SORT papers. + State vector contains `x_center`, `y_center` (box center), `scale` (area), + `aspect_ratio` (width/height), and velocities `vx`, `vy`, `vs`. Aspect ratio + is treated as constant (no velocity term), which works well for rigid objects + that maintain their shape. Matches the representation used in the original + SORT and OC-SORT papers. """ def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: @@ -177,10 +182,12 @@ def clamp_velocity(self) -> None: class XYXYKalmanFilter(BaseKalmanFilter): - """Corner-based Kalman filter: `[x1, y1, x2, y2, vx1, vy1, vx2, vy2]`. + """Corner-based Kalman filter with 8 state dimensions and 4 measurements. - 8 state dimensions, 4 measurement dimensions. - All four coordinates carry their own velocity term. + State vector contains `x1`, `y1` (top-left corner), `x2`, `y2` (bottom-right + corner), and independent velocities `vx1`, `vy1`, `vx2`, `vy2` for each + coordinate. This allows the box shape to change over time, which may be + better suited for non-rigid or deformable objects. """ def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: @@ -247,7 +254,7 @@ def create_kalman_filter( initial_bbox: First detection `[x1, y1, x2, y2]`. Returns: - An initialised :class:`BaseKalmanFilter` wrapping a configured + An initialised `BaseKalmanFilter` wrapping a configured Kalman filter. Raises: From 4abffc6f0ae06c444207ce2b29bfda822b3b6189 Mon Sep 17 00:00:00 2001 From: SkalskiP Date: Tue, 24 Feb 2026 10:17:31 +0100 Subject: [PATCH 52/87] - Add functions to trackers/__init__.py exports - Add doctests with full input/output examples - Add unit tests covering conversions, edge cases, and round-trips - Add API documentation under Trackers > Utilities section --- docs/api/trackers.md | 6 ++ test/utils/test_converters.py | 185 ++++++++++++++++++++++++++++++++++ trackers/__init__.py | 3 + trackers/utils/converters.py | 94 +++++++++++------ 4 files changed, 259 insertions(+), 29 deletions(-) create mode 100644 test/utils/test_converters.py diff --git a/docs/api/trackers.md b/docs/api/trackers.md index 74eb9560..10a33058 100644 --- a/docs/api/trackers.md +++ b/docs/api/trackers.md @@ -11,3 +11,9 @@ ## OC-SORT ::: trackers.core.ocsort.tracker.OCSORTTracker + +## Utilities + +::: trackers.utils.converters.xyxy_to_xcycsr + +::: trackers.utils.converters.xcycsr_to_xyxy diff --git a/test/utils/test_converters.py b/test/utils/test_converters.py new file mode 100644 index 00000000..3b234cbb --- /dev/null +++ b/test/utils/test_converters.py @@ -0,0 +1,185 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +from __future__ import annotations + +import numpy as np +import pytest + +from trackers.utils.converters import xcycsr_to_xyxy, xyxy_to_xcycsr + + +@pytest.mark.parametrize( + ("xyxy", "expected"), + [ + # Unit square at origin + ( + np.array([0.0, 0.0, 1.0, 1.0]), + np.array([0.5, 0.5, 1.0, 1.0]), + ), + # Rectangle 2x4 at (10, 20) + ( + np.array([10.0, 20.0, 12.0, 24.0]), + np.array([11.0, 22.0, 8.0, 0.5]), + ), + # Wide rectangle (aspect ratio > 1) + ( + np.array([0.0, 0.0, 100.0, 50.0]), + np.array([50.0, 25.0, 5000.0, 2.0]), + ), + # Tall rectangle (aspect ratio < 1) + ( + np.array([5.0, 5.0, 15.0, 55.0]), + np.array([10.0, 30.0, 500.0, 0.2]), + ), + # Negative coordinates (box crossing origin) + ( + np.array([-5.0, -5.0, 5.0, 5.0]), + np.array([0.0, 0.0, 100.0, 1.0]), + ), + # Very small box (sub-pixel) - aspect ratio affected by epsilon protection + ( + np.array([0.0, 0.0, 0.001, 0.001]), + np.array([0.0005, 0.0005, 0.000001, 0.999001]), + ), + # Very large box + ( + np.array([0.0, 0.0, 10000.0, 10000.0]), + np.array([5000.0, 5000.0, 100000000.0, 1.0]), + ), + ], +) +def test_xyxy_to_xcycsr_single_box(xyxy: np.ndarray, expected: np.ndarray) -> None: + result = xyxy_to_xcycsr(xyxy) + assert result.shape == (4,) + np.testing.assert_array_almost_equal(result, expected, decimal=5) + + +def test_xyxy_to_xcycsr_zero_height_returns_finite() -> None: + xyxy = np.array([0.0, 0.0, 10.0, 0.0]) + result = xyxy_to_xcycsr(xyxy) + assert np.isfinite(result).all() + assert result[2] == 0.0 + + +def test_xyxy_to_xcycsr_single_row_2d_returns_2d() -> None: + xyxy = np.array([[0.0, 0.0, 1.0, 1.0]]) + result = xyxy_to_xcycsr(xyxy) + assert result.shape == (1, 4) + np.testing.assert_array_almost_equal( + result[0], np.array([0.5, 0.5, 1.0, 1.0]), decimal=5 + ) + + +def test_xyxy_to_xcycsr_empty_batch_returns_empty() -> None: + xyxy = np.zeros((0, 4)) + result = xyxy_to_xcycsr(xyxy) + assert result.shape == (0, 4) + + +@pytest.mark.parametrize( + ("xcycsr", "expected"), + [ + # Unit square at (0.5, 0.5) + ( + np.array([0.5, 0.5, 1.0, 1.0]), + np.array([0.0, 0.0, 1.0, 1.0]), + ), + # Rectangle at (11, 22) with area=8, ratio=0.5 + ( + np.array([11.0, 22.0, 8.0, 0.5]), + np.array([10.0, 20.0, 12.0, 24.0]), + ), + # Wide box + ( + np.array([50.0, 25.0, 5000.0, 2.0]), + np.array([0.0, 0.0, 100.0, 50.0]), + ), + # Tall box + ( + np.array([10.0, 30.0, 500.0, 0.2]), + np.array([5.0, 5.0, 15.0, 55.0]), + ), + # Center at origin + ( + np.array([0.0, 0.0, 100.0, 1.0]), + np.array([-5.0, -5.0, 5.0, 5.0]), + ), + # Very small box + ( + np.array([0.0005, 0.0005, 0.000001, 1.0]), + np.array([0.0, 0.0, 0.001, 0.001]), + ), + # Very large box + ( + np.array([5000.0, 5000.0, 100000000.0, 1.0]), + np.array([0.0, 0.0, 10000.0, 10000.0]), + ), + ], +) +def test_xcycsr_to_xyxy_single_box(xcycsr: np.ndarray, expected: np.ndarray) -> None: + result = xcycsr_to_xyxy(xcycsr) + assert result.shape == (4,) + np.testing.assert_array_almost_equal(result, expected, decimal=4) + + +def test_xcycsr_to_xyxy_zero_scale_produces_nan() -> None: + xcycsr = np.array([10.0, 20.0, 0.0, 1.0]) + result = xcycsr_to_xyxy(xcycsr) + assert result[0] == result[2] == 10.0 + assert np.isnan(result[1]) and np.isnan(result[3]) + + +def test_xcycsr_to_xyxy_zero_aspect_ratio_produces_nan() -> None: + xcycsr = np.array([10.0, 20.0, 100.0, 0.0]) + result = xcycsr_to_xyxy(xcycsr) + assert np.isnan(result).any() or np.isinf(result).any() + + +def test_xcycsr_to_xyxy_negative_scale_produces_nan() -> None: + xcycsr = np.array([10.0, 20.0, -100.0, 1.0]) + result = xcycsr_to_xyxy(xcycsr) + assert np.isnan(result).any() + + +def test_xcycsr_to_xyxy_single_row_2d_returns_2d() -> None: + xcycsr = np.array([[0.5, 0.5, 1.0, 1.0]]) + result = xcycsr_to_xyxy(xcycsr) + assert result.shape == (1, 4) + np.testing.assert_array_almost_equal( + result[0], np.array([0.0, 0.0, 1.0, 1.0]), decimal=5 + ) + + +def test_xcycsr_to_xyxy_empty_batch_returns_empty() -> None: + xcycsr = np.zeros((0, 4)) + result = xcycsr_to_xyxy(xcycsr) + assert result.shape == (0, 4) + + +@pytest.mark.parametrize( + "xyxy", + [ + np.array([0.0, 0.0, 1.0, 1.0]), + np.array([10.0, 20.0, 30.0, 50.0]), + np.array([100.0, 200.0, 150.0, 210.0]), + np.array([-10.0, -20.0, 10.0, 20.0]), + np.array([0.0, 0.0, 0.01, 0.01]), + np.array([0.0, 0.0, 1000.0, 500.0]), + ], +) +def test_xyxy_xcycsr_roundtrip(xyxy: np.ndarray) -> None: + xcycsr = xyxy_to_xcycsr(xyxy) + recovered = xcycsr_to_xyxy(xcycsr) + np.testing.assert_array_almost_equal(recovered, xyxy, decimal=5) + + +def test_xyxy_xcycsr_roundtrip_preserves_2d_shape() -> None: + xyxy = np.array([[0.0, 0.0, 10.0, 10.0]]) + xcycsr = xyxy_to_xcycsr(xyxy) + recovered = xcycsr_to_xyxy(xcycsr) + assert recovered.shape == (1, 4) + np.testing.assert_array_almost_equal(recovered, xyxy, decimal=5) diff --git a/trackers/__init__.py b/trackers/__init__.py index b3180985..548442ca 100644 --- a/trackers/__init__.py +++ b/trackers/__init__.py @@ -15,6 +15,7 @@ HomographyTransformation, IdentityTransformation, ) +from trackers.utils.converters import xcycsr_to_xyxy, xyxy_to_xcycsr __all__ = [ "ByteTrackTracker", @@ -26,4 +27,6 @@ "OCSORTTracker", "SORTTracker", "frames_from_source", + "xcycsr_to_xyxy", + "xyxy_to_xcycsr", ] diff --git a/trackers/utils/converters.py b/trackers/utils/converters.py index bb28bfa3..32c06451 100644 --- a/trackers/utils/converters.py +++ b/trackers/utils/converters.py @@ -8,44 +8,80 @@ def xyxy_to_xcycsr(xyxy: np.ndarray) -> np.ndarray: - """ - Converts bounding box coordinates from `(x_min, y_min, x_max, y_max)` - format to `(x_center, y_center, scale, aspect_ratio)` format. + """Convert bounding boxes from corner to center-scale-ratio format. Args: - xyxy: A numpy array of shape `(4)` which corresponds to a bounding box - in the format `(x_min, y_min, x_max, y_max)`. + xyxy: Bounding boxes `[x_min, y_min, x_max, y_max]` with shape `(4,)` + for a single box or `(N, 4)` for multiple boxes. Returns: - A numpy array of shape `(4)` that corresponds to a bounding box - in the format `(x_min, y_min, x_max, y_max)`. + Bounding boxes `[x_center, y_center, scale, aspect_ratio]` with same + shape as input, where `scale` is area (`width * height`) and + `aspect_ratio` is `width / height`. + + Examples: + >>> import numpy as np + >>> from trackers import xyxy_to_xcycsr + >>> + >>> boxes = np.array([ + ... [0, 0, 10, 10], + ... [0, 0, 20, 10], + ... [0, 0, 10, 20], + ... ]) + >>> + >>> xyxy_to_xcycsr(boxes) + array([[ 5. , 5. , 100. , 0.9999999 ], + [ 10. , 5. , 200. , 1.9999998 ], + [ 5. , 10. , 200. , 0.49999998]]) """ - w = xyxy[2] - xyxy[0] - h = xyxy[3] - xyxy[1] - x = xyxy[0] + w / 2.0 - y = xyxy[1] + h / 2.0 + single = xyxy.ndim == 1 + if single: + xyxy = xyxy[np.newaxis, :] + + w = xyxy[:, 2] - xyxy[:, 0] + h = xyxy[:, 3] - xyxy[:, 1] + x = xyxy[:, 0] + w / 2.0 + y = xyxy[:, 1] + h / 2.0 s = w * h - r = w / float(h + 1e-6) - return np.array([x, y, s, r]) + r = w / (h + 1e-6) + + result = np.stack([x, y, s, r], axis=1) + return result[0] if single else result def xcycsr_to_xyxy(xcycsr: np.ndarray) -> np.ndarray: - """ - Takes a bounding box in the centre form [x,y,s,r] and returns it in the form - [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right + """Convert bounding boxes from center-scale-ratio to corner format. + Args: - xcycsr: A numpy array of shape `(4)` which corresponds to a centered bounding - box in the form [x,y,s,r] + xcycsr: Bounding boxes `[x_center, y_center, scale, aspect_ratio]` with + shape `(4,)` for a single box or `(N, 4)` for multiple boxes, + where `scale` is area and `aspect_ratio` is `width / height`. + Returns: - np.ndarray: bounding box in the form [x1,y1,x2,y2] + Bounding boxes `[x_min, y_min, x_max, y_max]` with same shape as input. + + Examples: + >>> import numpy as np + >>> from trackers import xcycsr_to_xyxy + >>> + >>> boxes = np.array([ + ... [ 5., 5., 100., 1.], + ... [ 10., 5., 200., 2.], + ... [ 5., 10., 200., 0.5], + ... ]) + >>> + >>> xcycsr_to_xyxy(boxes) + array([[ 0., 0., 10., 10.], + [ 0., 0., 20., 10.], + [ 0., 0., 10., 20.]]) """ - w = np.sqrt(xcycsr[2] * xcycsr[3]) - h = xcycsr[2] / w - return np.array( - [ - xcycsr[0] - w / 2.0, - xcycsr[1] - h / 2.0, - xcycsr[0] + w / 2.0, - xcycsr[1] + h / 2.0, - ] - ) + single = xcycsr.ndim == 1 + if single: + xcycsr = xcycsr[np.newaxis, :] + + w = np.sqrt(xcycsr[:, 2] * xcycsr[:, 3]) + h = xcycsr[:, 2] / w + x, y = xcycsr[:, 0], xcycsr[:, 1] + + result = np.stack([x - w / 2.0, y - h / 2.0, x + w / 2.0, y + h / 2.0], axis=1) + return result[0] if single else result From ee5fb546083a32b360fc2eaa1c55173f2221b6d1 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 24 Feb 2026 10:48:33 -0300 Subject: [PATCH 53/87] Applying changes requested by Piotr --- trackers/core/ocsort/tracker.py | 56 ++++++++++++------------- trackers/core/ocsort/tracklet.py | 4 -- trackers/core/ocsort/utils.py | 29 +------------ trackers/utils/state_representations.py | 38 ++++++++--------- 4 files changed, 47 insertions(+), 80 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index b120bd2a..666f434e 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -13,10 +13,9 @@ from trackers.core.base import BaseTracker from trackers.core.ocsort.tracklet import OCSORTTracklet from trackers.core.ocsort.utils import ( - add_track_id_detections, + add_track_id_to_detections, build_direction_consistency_matrix_batch, get_iou_matrix, - get_iou_matrix_between_boxes, ) from trackers.utils.state_representations import XCYCSRKalmanFilter @@ -93,7 +92,7 @@ def _get_associated_indices( direction_consistency_matrix: np.ndarray, ) -> tuple[list[tuple[int, int]], list[int], list[int]]: """ - Associate detections to tracks based on IOU + Associate detections to tracks based on IOU. Args: iou_matrix: IOU cost matrix. @@ -133,20 +132,17 @@ def _get_associated_indices( def _spawn_new_tracklets( self, detections: sv.Detections, - unmatched_detections: list[int], ) -> None: """ - Create new tracklets only for unmatched detections with confidence - above threshold. + Create new tracklets only for passed detections. It is used for activating + new tracklets from unmatched detections after association. Args: - detections: The latest set of object detections. - detection_boxes: Detected bounding boxes in the - form [x1, y1, x2, y2]. + detections: The detections that will start new tracklets with its coordinates. """ - for detection_idx in unmatched_detections: + for xyxy in detections.xyxy: new_tracker = OCSORTTracklet( - detections.xyxy[detection_idx], + xyxy, delta_t=self.delta_t, kalman_filter_class=self.kalman_filter_class, ) @@ -203,7 +199,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: # Update matched trackers with assigned detections for row, col in matched_indices: self.tracks[row].update(detection_boxes[col]) - add_track_id_detections( + add_track_id_to_detections( self.tracks[row], detections[col : col + 1], updated_detections, @@ -218,7 +214,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: [self.tracks[t_id].last_observation for t_id in unmatched_tracks] ) - ocr_iou_matrix = get_iou_matrix_between_boxes( + ocr_iou_matrix = sv.box_iou_batch( last_observation_of_tracks, detection_boxes[unmatched_detections] ) @@ -233,7 +229,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: track_idx = unmatched_tracks[ocr_row] det_idx = unmatched_detections[ocr_col] self.tracks[track_idx].update(detection_boxes[det_idx]) - add_track_id_detections( + add_track_id_to_detections( self.tracks[track_idx], detections[det_idx : det_idx + 1], updated_detections, @@ -245,25 +241,27 @@ def update(self, detections: sv.Detections) -> sv.Detections: for m in _ocr_unmatched_tracks: self.tracks[unmatched_tracks[m]].update(None) - self.tracks = self.activate_or_kill_tracklets() - self._spawn_new_tracklets( - detections[unmatched_detections], ocr_unmatched_detections - ) - left_detections = detections[unmatched_detections][ocr_unmatched_detections] - left_detections.tracker_id = np.array( - [-1] * len(left_detections), dtype=int + self.tracks = self._activate_or_kill_tracklets() + remaining_detections = detections[unmatched_detections][ + ocr_unmatched_detections + ] + + self._spawn_new_tracklets(remaining_detections) + remaining_detections.tracker_id = np.array( + [-1] * len(remaining_detections), dtype=int ) - updated_detections.append(left_detections) + updated_detections.append(remaining_detections) else: for track_idx in unmatched_tracks: self.tracks[track_idx].update(None) - self.tracks = self.activate_or_kill_tracklets() - self._spawn_new_tracklets(detections, unmatched_detections) - left_detections = detections[unmatched_detections] - left_detections.tracker_id = np.array( - [-1] * len(left_detections), dtype=int + self.tracks = self._activate_or_kill_tracklets() + remaining_detections = detections[unmatched_detections] + + self._spawn_new_tracklets(remaining_detections) + remaining_detections.tracker_id = np.array( + [-1] * len(remaining_detections), dtype=int ) - updated_detections.append(left_detections) + updated_detections.append(remaining_detections) final_updated_detections = sv.Detections.merge(updated_detections) if len(final_updated_detections) == 0: final_updated_detections.tracker_id = np.array([], dtype=int) @@ -279,7 +277,7 @@ def reset(self) -> None: self.frame_count = 0 OCSORTTracklet.count_id = 0 - def activate_or_kill_tracklets(self): + def _activate_or_kill_tracklets(self): """Activates or kills tracklets based on their status. This method checks each tracklet's status and either activates it diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 0cc2abc9..bf302782 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -270,10 +270,6 @@ def predict(self) -> np.ndarray: self.time_since_update += 1 return self.kalman_filter.state_to_bbox() - def is_lost(self) -> bool: - """Check if tracklet is considered lost.""" - return self.time_since_update > 1 - def get_state_bbox(self) -> np.ndarray: """Get current bounding box estimate from Kalman filter. diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 043d4c00..c6595ce0 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -19,33 +19,6 @@ from trackers.core.ocsort.tracklet import OCSORTTracklet -def k_previous_obs( - observations: dict[int, np.ndarray], cur_age: int, delta_t: int -) -> np.ndarray | None: - """Get observation from delta_t steps ago from the observations dict. - - Looks back up to delta_t timesteps. Falls back to the most recent - observation if none found in the lookback window. - - Args: - observations: Dict mapping age to observed bbox. - cur_age: Current age of the tracklet. - delta_t: Number of timesteps to look back. - - Returns: - The observation from delta_t steps ago, or the most recent one, - or None if no observations exist. - """ - if len(observations) == 0: - return None - for i in range(delta_t): - dt = delta_t - i - if cur_age - dt in observations: - return observations[cur_age - dt] - max_age = max(observations.keys()) - return observations[max_age] - - def _speed_direction(bbox1: np.ndarray, bbox2: np.ndarray) -> np.ndarray: """Compute normalized direction vector between two bounding box centers. @@ -203,7 +176,7 @@ def build_direction_consistency_matrix_batch( return angle_diff_cost.astype(np.float32) -def add_track_id_detections( +def add_track_id_to_detections( track: OCSORTTracklet, detection: sv.Detections, updated_detections: list[sv.Detections], diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py index f04e5f3b..52298334 100644 --- a/trackers/utils/state_representations.py +++ b/trackers/utils/state_representations.py @@ -44,20 +44,20 @@ class BaseKalmanFilter(ABC): kf: The underlying Kalman filter instance. """ - def __init__(self, initial_bbox: np.ndarray) -> None: + def __init__(self, bbox: np.ndarray) -> None: """Initialise the filter with the first detection. Args: - initial_bbox: First detection `[x1, y1, x2, y2]`. + bbox: First detection `[x1, y1, x2, y2]`. """ - self.kf: KalmanFilter = self._create_filter(initial_bbox) + self.kf: KalmanFilter = self._create_filter(bbox) @abstractmethod - def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: - """Create and configure a Kalman filter for *initial_bbox*. + def _create_filter(self, bbox: np.ndarray) -> KalmanFilter: + """Create and configure a Kalman filter for *bbox*. Args: - initial_bbox: First detection `[x1, y1, x2, y2]`. + bbox: First detection `[x1, y1, x2, y2]`. Returns: A fully configured :class:`KalmanFilter`. @@ -132,7 +132,7 @@ class XCYCSRKalmanFilter(BaseKalmanFilter): Matches the representation used in the original SORT and OC-SORT papers. """ - def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: + def _create_filter(self, bbox: np.ndarray) -> KalmanFilter: kf = KalmanFilter(dim_x=7, dim_z=4) # State transition: constant velocity model @@ -160,7 +160,7 @@ def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: kf.Q[4:, 4:] *= 0.01 # Initialise state with first observation - kf.x[:4] = xyxy_to_xcycsr(initial_bbox).reshape((4, 1)) + kf.x[:4] = xyxy_to_xcycsr(bbox).reshape((4, 1)) return kf @@ -173,7 +173,7 @@ def state_to_bbox(self) -> np.ndarray: def clamp_velocity(self) -> None: # If predicted scale would go negative, zero out scale velocity if (self.kf.x[6] + self.kf.x[2]) <= 0: - self.kf.x[6] *= 0.0 + self.kf.x[6] = 0.0 class XYXYKalmanFilter(BaseKalmanFilter): @@ -183,7 +183,7 @@ class XYXYKalmanFilter(BaseKalmanFilter): All four coordinates carry their own velocity term. """ - def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: + def _create_filter(self, bbox: np.ndarray) -> KalmanFilter: kf = KalmanFilter(dim_x=8, dim_z=4) # State transition: constant velocity for all coordinates @@ -211,7 +211,7 @@ def _create_filter(self, initial_bbox: np.ndarray) -> KalmanFilter: kf.Q[4:, 4:] *= 0.01 # Initialise state with first observation (direct XYXY) - kf.x[:4] = initial_bbox.reshape((4, 1)) + kf.x[:4] = bbox.reshape((4, 1)) return kf @@ -231,20 +231,20 @@ def clamp_velocity(self) -> None: # --------------------------------------------------------------------------- _REPR_MAP: dict[str, type[BaseKalmanFilter]] = { - "xcycsr": XCYCSRKalmanFilter, - "xyxy": XYXYKalmanFilter, + StateRepresentation.XCYCSR: XCYCSRKalmanFilter, + StateRepresentation.XYXY: XYXYKalmanFilter, } def create_kalman_filter( - state_repr: str, - initial_bbox: np.ndarray, + state_repr: StateRepresentation, + bbox: np.ndarray, ) -> BaseKalmanFilter: """Create a Kalman filter for the given state representation. Args: - state_repr: The desired representation (`"xcycsr"` or `"xyxy"`). - initial_bbox: First detection `[x1, y1, x2, y2]`. + state_repr: The desired representation. Ex: StateRepresentation.XCYCSR + bbox: First detection `[x1, y1, x2, y2]`. Returns: An initialised :class:`BaseKalmanFilter` wrapping a configured @@ -253,10 +253,10 @@ def create_kalman_filter( Raises: ValueError: If *state_repr* is not recognised. """ - cls = _REPR_MAP.get(state_repr) + cls = _REPR_MAP.get(state_repr, None) if cls is None: raise ValueError( f"Unknown state representation: {state_repr!r}. " f"Available: {list(_REPR_MAP.keys())}" ) - return cls(initial_bbox) + return cls(bbox) From 81b34dbb1bb1244f66b722d5b5026ced6288f692 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 24 Feb 2026 10:57:26 -0300 Subject: [PATCH 54/87] Fixed pre commit --- trackers/core/ocsort/tracker.py | 2 +- trackers/utils/state_representations.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 42927f1a..9fa042a5 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -142,7 +142,7 @@ def _spawn_new_tracklets( new tracklets from unmatched detections after association. Args: - detections: The detections that will start new tracklets with its coordinates. + detections: The detections that will start new tracklets. """ for xyxy in detections.xyxy: new_tracker = OCSORTTracklet( diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py index 13842f5a..ae855f4d 100644 --- a/trackers/utils/state_representations.py +++ b/trackers/utils/state_representations.py @@ -237,7 +237,7 @@ def clamp_velocity(self) -> None: # Factory helper # --------------------------------------------------------------------------- -_REPR_MAP: dict[str, type[BaseKalmanFilter]] = { +_REPR_MAP: dict[StateRepresentation, type[BaseKalmanFilter]] = { StateRepresentation.XCYCSR: XCYCSRKalmanFilter, StateRepresentation.XYXY: XYXYKalmanFilter, } From 7a95de091cf5ff519ef62f2a3eb0b35c0f1bed2c Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 24 Feb 2026 10:58:54 -0300 Subject: [PATCH 55/87] try removing activate tracklets in activate_or_kill_tracklets as it seemed redundant because oc-sort activates them earlier --- trackers/core/ocsort/tracker.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 9fa042a5..48b6404f 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -298,6 +298,4 @@ def _activate_or_kill_tracklets(self): if tracklet.time_since_update <= self.maximum_frames_without_update: alive_tracklets.append(tracklet) - if is_mature and tracklet.tracker_id == -1: - tracklet.tracker_id = OCSORTTracklet.get_next_tracker_id() return alive_tracklets From ae1fc86ca2bd5335e81cc3c557f8ef4d94be7da9 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 24 Feb 2026 11:45:12 -0300 Subject: [PATCH 56/87] completely corrected activate_or_kill_tracklets to kill_tracklets after evaluating in sportsmot and getting same metrics --- trackers/core/ocsort/tracker.py | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 48b6404f..2701cc1c 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -245,7 +245,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: for m in _ocr_unmatched_tracks: self.tracks[unmatched_tracks[m]].update(None) - self.tracks = self._activate_or_kill_tracklets() + self.tracks = self._kill_tracklets() remaining_detections = detections[unmatched_detections][ ocr_unmatched_detections ] @@ -258,7 +258,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: else: for track_idx in unmatched_tracks: self.tracks[track_idx].update(None) - self.tracks = self._activate_or_kill_tracklets() + self.tracks = self._kill_tracklets() remaining_detections = detections[unmatched_detections] self._spawn_new_tracklets(remaining_detections) @@ -281,20 +281,16 @@ def reset(self) -> None: self.frame_count = 0 OCSORTTracklet.count_id = 0 - def _activate_or_kill_tracklets(self): - """Activates or kills tracklets based on their status. + def _kill_tracklets(self): + """Kills tracklets that have been lost for too long. - This method checks each tracklet's status and either activates it - (assigning a tracker ID) if it meets the criteria for being a valid - track, or kills it (removing it from active tracking) if it has been + This method checks each tracklet's status and either keeps it + active if it meets the criteria for being a valid track, + or kills it (removing it from active tracking) if it has been lost for too long. """ alive_tracklets = [] for tracklet in self.tracks: - is_mature = ( - tracklet.number_of_successful_consecutive_updates - >= self.minimum_consecutive_frames - ) if tracklet.time_since_update <= self.maximum_frames_without_update: alive_tracklets.append(tracklet) From b35e353cf38bfd06da2a13e6bf411bc5c4eb07ec Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 24 Feb 2026 13:48:01 -0300 Subject: [PATCH 57/87] changed BaseKalmanFilter to BaseStateEstimator and same for other types of StateKalmanFilters --- trackers/core/ocsort/tracker.py | 6 +++--- trackers/core/ocsort/tracklet.py | 12 ++++++------ trackers/core/ocsort/utils.py | 25 ------------------------- trackers/utils/state_representations.py | 22 +++++++++++----------- 4 files changed, 20 insertions(+), 45 deletions(-) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 2701cc1c..da117581 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -17,7 +17,7 @@ build_direction_consistency_matrix_batch, get_iou_matrix, ) -from trackers.utils.state_representations import XCYCSRKalmanFilter +from trackers.utils.state_representations import XCYCSRStateEstimator class OCSORTTracker(BaseTracker): @@ -88,7 +88,7 @@ def __init__( self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 - self.kalman_filter_class = XCYCSRKalmanFilter + self.state_estimator_class = XCYCSRStateEstimator def _get_associated_indices( self, @@ -148,7 +148,7 @@ def _spawn_new_tracklets( new_tracker = OCSORTTracklet( xyxy, delta_t=self.delta_t, - kalman_filter_class=self.kalman_filter_class, + state_estimator_class=self.state_estimator_class, ) self.tracks.append(new_tracker) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 7ce0fa6f..aafe7a18 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -12,8 +12,8 @@ xyxy_to_xcycsr, ) from trackers.utils.state_representations import ( - BaseKalmanFilter, - XCYCSRKalmanFilter, + BaseStateEstimator, + XCYCSRStateEstimator, ) @@ -43,7 +43,7 @@ class OCSORTTracklet: def __init__( self, initial_bbox: np.ndarray, - kalman_filter_class: type[BaseKalmanFilter] = XCYCSRKalmanFilter, + state_estimator_class: type[BaseStateEstimator] = XCYCSRStateEstimator, delta_t: int = 3, ) -> None: """Initialize tracklet with first detection. @@ -59,8 +59,8 @@ def __init__( """ self.age = 0 - # Initialize Kalman filter (wraps raw KalmanFilter + state repr) - self.kalman_filter: BaseKalmanFilter = kalman_filter_class(initial_bbox) + # Initialize state estimator (wraps KalmanFilter + state repr) + self.kalman_filter: BaseStateEstimator = state_estimator_class(initial_bbox) # Observation history for ORU and delta_t self.delta_t = delta_t @@ -110,7 +110,7 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: time_gap = self.time_since_update # this is oc-sort specific - if isinstance(self.kalman_filter, XCYCSRKalmanFilter): + if isinstance(self.kalman_filter, XCYCSRStateEstimator): self._unfreeze_xcycsr(new_bbox, time_gap) else: self._unfreeze_xyxy(new_bbox, time_gap) diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 9694a789..ee8d2a85 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -242,28 +242,3 @@ def get_iou_matrix( iou_matrix = np.zeros((len(trackers), len(detection_boxes)), dtype=np.float32) return iou_matrix - - -def get_iou_matrix_between_boxes( - last_observations: np.ndarray, detection_boxes: np.ndarray -) -> np.ndarray: - """Build IOU cost matrix between two sets of bounding boxes. - - Args: - last_observations: First set of boxes `[x1, y1, x2, y2]`. - detection_boxes: Second set of boxes `[x1, y1, x2, y2]`. - - Returns: - np.ndarray: IOU matrix of shape (n_observations, n_detections). - """ - if len(last_observations) == 0 and len(last_observations) > 0: - last_observations = np.zeros((len(last_observations), 4), dtype=np.float32) - - if len(last_observations) > 0 and len(detection_boxes) > 0: - iou_matrix = sv.box_iou_batch(last_observations, detection_boxes) - else: - iou_matrix = np.zeros( - (len(last_observations), len(detection_boxes)), dtype=np.float32 - ) - - return iou_matrix diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py index ae855f4d..f1e93a22 100644 --- a/trackers/utils/state_representations.py +++ b/trackers/utils/state_representations.py @@ -34,7 +34,7 @@ class StateRepresentation(Enum): XYXY = "xyxy" -class BaseKalmanFilter(ABC): +class BaseStateEstimator(ABC): """Abstract Kalman filter with a specific bounding box state representation. Wraps a `KalmanFilter` and provides a unified interface for @@ -127,7 +127,7 @@ def set_state(self, state: dict) -> None: self.kf.set_state(state) -class XCYCSRKalmanFilter(BaseKalmanFilter): +class XCYCSRStateEstimator(BaseStateEstimator): """Center-based Kalman filter with 7 state dimensions and 4 measurements. State vector contains `x_center`, `y_center` (box center), `scale` (area), @@ -181,7 +181,7 @@ def clamp_velocity(self) -> None: self.kf.x[6] = 0.0 -class XYXYKalmanFilter(BaseKalmanFilter): +class XYXYStateEstimator(BaseStateEstimator): """Corner-based Kalman filter with 8 state dimensions and 4 measurements. State vector contains `x1`, `y1` (top-left corner), `x2`, `y2` (bottom-right @@ -237,25 +237,25 @@ def clamp_velocity(self) -> None: # Factory helper # --------------------------------------------------------------------------- -_REPR_MAP: dict[StateRepresentation, type[BaseKalmanFilter]] = { - StateRepresentation.XCYCSR: XCYCSRKalmanFilter, - StateRepresentation.XYXY: XYXYKalmanFilter, +_REPR_MAP: dict[StateRepresentation, type[BaseStateEstimator]] = { + StateRepresentation.XCYCSR: XCYCSRStateEstimator, + StateRepresentation.XYXY: XYXYStateEstimator, } -def create_kalman_filter( +def create_state_estimator( state_repr: StateRepresentation, bbox: np.ndarray, -) -> BaseKalmanFilter: - """Create a Kalman filter for the given state representation. +) -> BaseStateEstimator: + """Create a state estimator for the given state representation. Args: state_repr: The desired representation. Ex: StateRepresentation.XCYCSR bbox: First detection `[x1, y1, x2, y2]`. Returns: - An initialised `BaseKalmanFilter` wrapping a configured - Kalman filter. + An initialised `BaseStateEstimator` wrapping a configured + estimator. Raises: ValueError: If *state_repr* is not recognised. From 081c4e28d298d182004850232c45adaa532cbfd8 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 25 Feb 2026 14:38:05 -0300 Subject: [PATCH 58/87] initial commit adding abstract BaseTraclet and inheriting it in ocsort's tracklet --- trackers/core/ocsort/tracklet.py | 23 +++-------------- trackers/utils/base_tracklet.py | 44 ++++++++++++++++++++++++++++++++ 2 files changed, 47 insertions(+), 20 deletions(-) create mode 100644 trackers/utils/base_tracklet.py diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index aafe7a18..ba311a6f 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -15,9 +15,9 @@ BaseStateEstimator, XCYCSRStateEstimator, ) +from trackers.utils.base_tracklet import BaseTracklet - -class OCSORTTracklet: +class OCSORTTracklet(BaseTracklet): """Tracklet for OC-SORT tracker with ORU (Observation-centric Re-Update). Manages a single tracked object with Kalman filter state estimation. @@ -57,11 +57,9 @@ def __init__( Higher values use observations further in the past to estimate motion direction, providing more stable velocity estimates. """ - self.age = 0 # Initialize state estimator (wraps KalmanFilter + state repr) - self.kalman_filter: BaseStateEstimator = state_estimator_class(initial_bbox) - + super().__init__(initial_bbox, state_estimator_class) # Observation history for ORU and delta_t self.delta_t = delta_t self.last_observation = initial_bbox @@ -69,25 +67,10 @@ def __init__( self.observations: dict[int, np.ndarray] = {} self.velocity: np.ndarray | None = None - # Track ID can be initialized before mature in oc-sort - # it is assigned if the frame number is less than minimum_consecutive_frames - self.tracker_id = -1 - - # Tracking counters - self.number_of_successful_consecutive_updates = 0 - self.time_since_update = 0 - # ORU: saved state for freeze/unfreeze self._frozen_state: dict | None = None self._observed = True - @classmethod - def get_next_tracker_id(cls) -> int: - """Get next available tracker ID.""" - next_id = cls.count_id - cls.count_id += 1 - return next_id - def _freeze(self) -> None: """Save Kalman filter state before track is lost (ORU mechanism).""" self._frozen_state = self.kalman_filter.get_state() diff --git a/trackers/utils/base_tracklet.py b/trackers/utils/base_tracklet.py new file mode 100644 index 00000000..392688f5 --- /dev/null +++ b/trackers/utils/base_tracklet.py @@ -0,0 +1,44 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +from abc import ABC, abstractmethod +import numpy as np +from trackers.utils.state_representations import BaseStateEstimator +class BaseTracklet(ABC): + """ + Abstract base class for all tracker-specific tracklets. + Provides common interface and attributes for tracklet management. + """ + count_id: int = 0 + + def __init__(self, bbox: np.ndarray, state_estimator_class: type[BaseStateEstimator] ) -> None: + self.age = 0 + self.kalman_filter: BaseStateEstimator = state_estimator_class(bbox) + + self.tracker_id = -1 + self.time_since_update = 0 + self.number_of_successful_consecutive_updates = 0 + + @classmethod + def get_next_tracker_id(cls) -> int: + next_id = cls.count_id + cls.count_id += 1 + return next_id + + @abstractmethod + def update(self, bbox: np.ndarray | None) -> None: + """Update tracklet with new observation or None if missed.""" + pass + + @abstractmethod + def predict(self) -> np.ndarray: + """Predict next bounding box position.""" + pass + + @abstractmethod + def get_state_bbox(self) -> np.ndarray: + """Get current bounding box estimate from the filter/state.""" + pass From d7ac41d3e10af774bd915ad16c027f258f9641ad Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 25 Feb 2026 19:36:47 -0300 Subject: [PATCH 59/87] sort might be done --- trackers/core/sort/tracker.py | 18 ++++++----- trackers/core/sort/tracklet.py | 38 +++++++++++++++++++++++ trackers/core/sort/utils.py | 55 +++++++++++++++------------------- 3 files changed, 73 insertions(+), 38 deletions(-) create mode 100644 trackers/core/sort/tracklet.py diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index 6e956a92..b3937615 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -11,10 +11,11 @@ from trackers.core.base import BaseTracker from trackers.core.sort.kalman import SORTKalmanBoxTracker from trackers.core.sort.utils import ( - get_alive_trackers, + get_alive_tracklets, get_iou_matrix, update_detections_with_track_ids, ) +from trackers.core.sort.tracklet import SORTTracklet class SORTTracker(BaseTracker): @@ -58,7 +59,7 @@ def __init__( self.track_activation_threshold = track_activation_threshold # Active trackers - self.trackers: list[SORTKalmanBoxTracker] = [] + self.trackers: list[SORTTracklet] = [] def _get_associated_indices( self, iou_matrix: np.ndarray, detection_boxes: np.ndarray @@ -112,7 +113,8 @@ def _spawn_new_trackers( or detections.confidence[detection_idx] >= self.track_activation_threshold ): - new_tracker = SORTKalmanBoxTracker(detection_boxes[detection_idx]) + new_tracker = SORTTracklet(detection_boxes[detection_idx], + ) self.trackers.append(new_tracker) def update(self, detections: sv.Detections) -> sv.Detections: @@ -147,7 +149,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: iou_matrix = get_iou_matrix(self.trackers, detection_boxes) # Associate detections to trackers based on IOU - matched_indices, _, unmatched_detections = self._get_associated_indices( + matched_indices, unmatched_trackers, unmatched_detections = self._get_associated_indices( iou_matrix, detection_boxes ) @@ -155,10 +157,13 @@ def update(self, detections: sv.Detections) -> sv.Detections: for row, col in matched_indices: self.trackers[row].update(detection_boxes[col]) + # Update non matched for increasing time_since_update + for index in unmatched_trackers: + self.trackers[index].update(None) self._spawn_new_trackers(detections, detection_boxes, unmatched_detections) # Remove dead trackers - self.trackers = get_alive_trackers( + self.trackers = get_alive_tracklets( self.trackers, self.minimum_consecutive_frames, self.maximum_frames_without_update, @@ -171,7 +176,6 @@ def update(self, detections: sv.Detections) -> sv.Detections: self.minimum_iou_threshold, self.minimum_consecutive_frames, ) - return updated_detections def reset(self) -> None: @@ -179,4 +183,4 @@ def reset(self) -> None: Call this method when switching to a new video or scene. """ self.trackers = [] - SORTKalmanBoxTracker.count_id = 0 + SORTTracklet.count_id = 0 diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py new file mode 100644 index 00000000..909fb42f --- /dev/null +++ b/trackers/core/sort/tracklet.py @@ -0,0 +1,38 @@ +from trackers.utils.state_representations import ( + BaseStateEstimator, + XCYCSRStateEstimator, +) +import numpy as np +from trackers.utils.base_tracklet import BaseTracklet + +class SORTTracklet(BaseTracklet): + count_id: int = 0 + + def __init__( + self, + initial_bbox: np.ndarray, + state_estimator_class: type[BaseStateEstimator] = XCYCSRStateEstimator, + ) -> None: + super().__init__(initial_bbox, state_estimator_class) + + def update(self, bbox: np.ndarray | None) -> None: + """Update tracklet with new observation or None if missed.""" + if bbox is not None: + self.kalman_filter.update(bbox) + self.time_since_update = 0 + self.number_of_successful_consecutive_updates += 1 + else: + self.kalman_filter.update(None) + self.time_since_update += 1 + self.number_of_successful_consecutive_updates = 0 + + def predict(self) -> np.ndarray: + """Predict next bounding box position.""" + self.kalman_filter.predict() + self.age += 1 + return self.kalman_filter.state_to_bbox() + + def get_state_bbox(self) -> np.ndarray: + """Get current bounding box estimate from the filter/state.""" + return self.kalman_filter.state_to_bbox() + diff --git a/trackers/core/sort/utils.py b/trackers/core/sort/utils.py index 987f90f4..acc31874 100644 --- a/trackers/core/sort/utils.py +++ b/trackers/core/sort/utils.py @@ -6,24 +6,17 @@ from collections.abc import Sequence from copy import deepcopy -from typing import TypeVar import numpy as np import supervision as sv -from trackers.core.bytetrack.kalman import ByteTrackKalmanBoxTracker -from trackers.core.sort.kalman import SORTKalmanBoxTracker +from trackers.utils.base_tracklet import BaseTracklet -KalmanBoxTrackerType = TypeVar( - "KalmanBoxTrackerType", bound=SORTKalmanBoxTracker | ByteTrackKalmanBoxTracker -) - - -def get_alive_trackers( - trackers: Sequence[KalmanBoxTrackerType], +def get_alive_tracklets( + trackers: Sequence[BaseTracklet], minimum_consecutive_frames: int, maximum_frames_without_update: int, -) -> list[KalmanBoxTrackerType]: +) -> list[BaseTracklet]: """ Remove dead or immature lost tracklets and get alive trackers that are within `maximum_frames_without_update` AND (it's mature OR @@ -41,7 +34,7 @@ def get_alive_trackers( """ alive_trackers = [] for tracker in trackers: - is_mature = tracker.number_of_successful_updates >= minimum_consecutive_frames + is_mature = tracker.number_of_successful_consecutive_updates >= minimum_consecutive_frames is_active = tracker.time_since_update == 0 if tracker.time_since_update < maximum_frames_without_update and ( is_mature or is_active @@ -51,34 +44,34 @@ def get_alive_trackers( def get_iou_matrix( - trackers: Sequence[KalmanBoxTrackerType], detection_boxes: np.ndarray + tracks: Sequence[BaseTracklet], detection_boxes: np.ndarray ) -> np.ndarray: """ Build IOU cost matrix between detections and predicted bounding boxes Args: - trackers: List of KalmanBoxTracker objects. + tracks: List of KalmanBoxTracker objects. detection_boxes: Detected bounding boxes in the form [x1, y1, x2, y2]. Returns: IOU cost matrix. """ - predicted_boxes = np.array([t.get_state_bbox() for t in trackers]) - if len(predicted_boxes) == 0 and len(trackers) > 0: + predicted_boxes = np.array([t.get_state_bbox() for t in tracks]) + if len(predicted_boxes) == 0 and len(tracks) > 0: # Handle case where get_state_bbox might return empty array - predicted_boxes = np.zeros((len(trackers), 4), dtype=np.float32) + predicted_boxes = np.zeros((len(tracks), 4), dtype=np.float32) - if len(trackers) > 0 and len(detection_boxes) > 0: + if len(tracks) > 0 and len(detection_boxes) > 0: iou_matrix = sv.box_iou_batch(predicted_boxes, detection_boxes) else: - iou_matrix = np.zeros((len(trackers), len(detection_boxes)), dtype=np.float32) + iou_matrix = np.zeros((len(tracks), len(detection_boxes)), dtype=np.float32) return iou_matrix def update_detections_with_track_ids( - trackers: Sequence[KalmanBoxTrackerType], + tracks: Sequence[BaseTracklet], detections: sv.Detections, detection_boxes: np.ndarray, minimum_iou_threshold: float, @@ -90,7 +83,7 @@ def update_detections_with_track_ids( it is assigned an ID to the detection that just updated it. Args: - trackers: List of SORTKalmanBoxTracker objects. + tracks: List of BaseTracklet objects. detections: The latest set of object detections. detection_boxes: Detected bounding boxes in the form [x1, y1, x2, y2]. @@ -106,16 +99,16 @@ def update_detections_with_track_ids( # Re-run association in the same way (could also store direct mapping) final_tracker_ids = [-1] * len(detection_boxes) - # Recalculate predicted_boxes based on current trackers after some may have + # Recalculate predicted_boxes based on current tracks after some may have # been removed - predicted_boxes = np.array([t.get_state_bbox() for t in trackers]) - iou_matrix_final = np.zeros((len(trackers), len(detection_boxes)), dtype=np.float32) + predicted_boxes = np.array([t.get_state_bbox() for t in tracks]) + iou_matrix_final = np.zeros((len(tracks), len(detection_boxes)), dtype=np.float32) # Ensure predicted_boxes is properly shaped before the second iou calculation - if len(predicted_boxes) == 0 and len(trackers) > 0: - predicted_boxes = np.zeros((len(trackers), 4), dtype=np.float32) + if len(predicted_boxes) == 0 and len(tracks) > 0: + predicted_boxes = np.zeros((len(tracks), 4), dtype=np.float32) - if len(trackers) > 0 and len(detection_boxes) > 0: + if len(tracks) > 0 and len(detection_boxes) > 0: iou_matrix_final = sv.box_iou_batch(predicted_boxes, detection_boxes) row_indices, col_indices = np.where(iou_matrix_final > minimum_iou_threshold) @@ -128,18 +121,18 @@ def update_detections_with_track_ids( used_cols: set[int] = set() for row, col in sorted_pairs: # Double check index is in range - if row < len(trackers): - tracker_obj = trackers[int(row)] + if row < len(tracks): + tracker_obj = tracks[int(row)] # Only assign if the track is "mature" or is new but has enough hits if (int(row) not in used_rows) and (int(col) not in used_cols): if ( - tracker_obj.number_of_successful_updates + tracker_obj.number_of_successful_consecutive_updates >= minimum_consecutive_frames ): # If tracker is mature but still has ID -1, assign a new ID if tracker_obj.tracker_id == -1: tracker_obj.tracker_id = ( - SORTKalmanBoxTracker.get_next_tracker_id() + type(tracker_obj).get_next_tracker_id() ) final_tracker_ids[int(col)] = tracker_obj.tracker_id used_rows.add(int(row)) From 2eb7f19d1ec9398e7f12beaa559cec21743a88aa Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 25 Feb 2026 19:43:11 -0300 Subject: [PATCH 60/87] tracker changed by tracklet in byte --- trackers/core/bytetrack/tracker.py | 24 ++++++++++++------------ trackers/core/sort/utils.py | 20 ++++++++++---------- 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/trackers/core/bytetrack/tracker.py b/trackers/core/bytetrack/tracker.py index a35ecf91..fb46d44f 100644 --- a/trackers/core/bytetrack/tracker.py +++ b/trackers/core/bytetrack/tracker.py @@ -14,7 +14,7 @@ from trackers.core.base import BaseTracker from trackers.core.bytetrack.kalman import ByteTrackKalmanBoxTracker from trackers.core.sort.utils import ( - get_alive_trackers, + get_alive_tracklets, get_iou_matrix, ) @@ -76,7 +76,7 @@ def _update_detections( for row, col in matched_indices: t = tracks[row] t.update(det_bboxes[col]) - # If tracker is mature but still has ID -1, assign a new ID + # If track is mature but still has ID -1, assign a new ID if ( t.number_of_successful_updates >= self.minimum_consecutive_frames and t.tracker_id == -1 @@ -94,7 +94,7 @@ def update( self, detections: sv.Detections, ) -> sv.Detections: - """Update tracker state with new detections and return tracked objects. + """Update tracks state with new detections and return tracked objects. Performs Kalman filter prediction, two-stage association (high then low confidence), and initializes new tracks for unmatched detections. @@ -118,8 +118,8 @@ def update( ] = [] # List for returning the updated detections with its new assigned track id # noqa: E501 # Predict new locations for existing tracks - for tracker in self.tracks: - tracker.predict() + for track in self.tracks: + track.predict() # Split into high confidence boxes and lower based on self.high_conf_det_threshold # noqa: E501 high_prob_detections, low_prob_detections = ( @@ -164,7 +164,7 @@ def update( new_det.tracker_id = np.array([-1]) updated_detections.append(new_det) - self._spawn_new_trackers( + self._spawn_new_tracks( high_prob_detections, high_prob_detections.xyxy, unmatched_high_prob_detections, @@ -172,8 +172,8 @@ def update( ) # Kill lost tracks - self.tracks = get_alive_trackers( - trackers=self.tracks, + self.tracks = get_alive_tracklets( + self.tracks maximum_frames_without_update=self.maximum_frames_without_update, minimum_consecutive_frames=self.minimum_consecutive_frames, ) @@ -248,7 +248,7 @@ def _get_associated_indices( return matched_indices, unmatched_tracks, unmatched_detections - def _spawn_new_trackers( + def _spawn_new_tracks( self, detections: sv.Detections, detection_boxes: np.ndarray, @@ -256,7 +256,7 @@ def _spawn_new_trackers( updated_detections: list[sv.Detections], ): """ - Create new trackers for unmatched detections and + Create new tracks for unmatched detections and append detections to updated_detections detections. Args: @@ -278,10 +278,10 @@ def _spawn_new_trackers( if confidence_score >= self.track_activation_threshold: # Original logic for high confidence detection - new_tracker = ByteTrackKalmanBoxTracker( + new_track = ByteTrackKalmanBoxTracker( bbox=detection_boxes[detection_idx] ) - self.tracks.append(new_tracker) + self.tracks.append(new_track) new_det = deepcopy(detections[detection_idx : detection_idx + 1]) new_det = cast(sv.Detections, new_det) # Cast added previously diff --git a/trackers/core/sort/utils.py b/trackers/core/sort/utils.py index acc31874..81352291 100644 --- a/trackers/core/sort/utils.py +++ b/trackers/core/sort/utils.py @@ -13,7 +13,7 @@ from trackers.utils.base_tracklet import BaseTracklet def get_alive_tracklets( - trackers: Sequence[BaseTracklet], + tracklets: Sequence[BaseTracklet], minimum_consecutive_frames: int, maximum_frames_without_update: int, ) -> list[BaseTracklet]: @@ -23,24 +23,24 @@ def get_alive_tracklets( it was just updated). Args: - trackers: List of KalmanBoxTracker objects. + tracklets: List of KalmanBoxTracker objects. minimum_consecutive_frames: Number of consecutive frames that an object must be tracked before it is considered a 'valid' track. maximum_frames_without_update: Maximum number of frames without update before a track is considered dead. Returns: - List of alive trackers. + List of alive tracklets. """ - alive_trackers = [] - for tracker in trackers: - is_mature = tracker.number_of_successful_consecutive_updates >= minimum_consecutive_frames - is_active = tracker.time_since_update == 0 - if tracker.time_since_update < maximum_frames_without_update and ( + alive_tracklets = [] + for tracklet in tracklets: + is_mature = tracklet.number_of_successful_consecutive_updates >= minimum_consecutive_frames + is_active = tracklet.time_since_update == 0 + if tracklet.time_since_update < maximum_frames_without_update and ( is_mature or is_active ): - alive_trackers.append(tracker) - return alive_trackers + alive_tracklets.append(tracklet) + return alive_tracklets def get_iou_matrix( From e1ba0e857f1d869e958cab364af9ea66039d64ba Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 26 Feb 2026 13:07:44 -0300 Subject: [PATCH 61/87] before merge --- trackers/core/bytetrack/tracker.py | 2 +- trackers/core/ocsort/tracker.py | 7 ++++--- trackers/core/ocsort/utils.py | 12 +++++------- trackers/core/sort/tracker.py | 1 - 4 files changed, 10 insertions(+), 12 deletions(-) diff --git a/trackers/core/bytetrack/tracker.py b/trackers/core/bytetrack/tracker.py index fb46d44f..7107482c 100644 --- a/trackers/core/bytetrack/tracker.py +++ b/trackers/core/bytetrack/tracker.py @@ -173,7 +173,7 @@ def update( # Kill lost tracks self.tracks = get_alive_tracklets( - self.tracks + self.tracks, maximum_frames_without_update=self.maximum_frames_without_update, minimum_consecutive_frames=self.minimum_consecutive_frames, ) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index da117581..3c4adab3 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -174,13 +174,14 @@ def update(self, detections: sv.Detections) -> sv.Detections: return result detections = detections[detections.confidence >= self.high_conf_det_threshold] + copied_detections = deepcopy(detections) updated_detections: list[ sv.Detections ] = [] # List for returning the updated detections # Convert detections to a (N x 4) array (x1, y1, x2, y2) detection_boxes = ( - detections.xyxy if len(detections) > 0 else np.array([]).reshape(0, 4) + copied_detections.xyxy if len(copied_detections) > 0 else np.array([]).reshape(0, 4) ) # Predict new locations for existing tracks KF @@ -205,7 +206,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: self.tracks[row].update(detection_boxes[col]) add_track_id_to_detections( self.tracks[row], - detections[col : col + 1], + copied_detections[col : col + 1], updated_detections, self.minimum_consecutive_frames, self.frame_count, @@ -235,7 +236,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: self.tracks[track_idx].update(detection_boxes[det_idx]) add_track_id_to_detections( self.tracks[track_idx], - detections[det_idx : det_idx + 1], + copied_detections[det_idx : det_idx + 1], updated_detections, self.minimum_consecutive_frames, self.frame_count, diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index ee8d2a85..90ef287f 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -193,13 +193,11 @@ def add_track_id_to_detections( Args: track: The tracklet being processed. - detection: The detection to assign an ID to. + detection: The detection to assign an ID to. It will be modified. updated_detections: List to append the updated detection to. minimum_consecutive_frames: Frames required for track maturity. frame_count: Current frame number in tracking process. """ - new_det = deepcopy(detection) - new_det = cast(sv.Detections, new_det) is_mature = ( track.number_of_successful_consecutive_updates >= minimum_consecutive_frames ) @@ -208,16 +206,16 @@ def add_track_id_to_detections( if track.tracker_id == -1: track.tracker_id = OCSORTTracklet.get_next_tracker_id() - new_det.tracker_id = np.array([track.tracker_id]) + detection.tracker_id = np.array([track.tracker_id]) else: if is_mature: # Assign ID now if track just became mature if track.tracker_id == -1: track.tracker_id = OCSORTTracklet.get_next_tracker_id() - new_det.tracker_id = np.array([track.tracker_id]) + detection.tracker_id = np.array([track.tracker_id]) else: - new_det.tracker_id = np.array([-1], dtype=int) - updated_detections.append(new_det) + detection.tracker_id = np.array([-1], dtype=int) + updated_detections.append(detection) def get_iou_matrix( diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index b3937615..13f46384 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -9,7 +9,6 @@ from scipy.optimize import linear_sum_assignment from trackers.core.base import BaseTracker -from trackers.core.sort.kalman import SORTKalmanBoxTracker from trackers.core.sort.utils import ( get_alive_tracklets, get_iou_matrix, From ef048d585a4bd20137da50c53b8c85615472978e Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 26 Feb 2026 14:22:29 -0300 Subject: [PATCH 62/87] possible optimization by copying detections only once per update --- trackers/core/ocsort/tracker.py | 1 + trackers/core/sort/kalman.py | 147 -------------------------------- 2 files changed, 1 insertion(+), 147 deletions(-) delete mode 100644 trackers/core/sort/kalman.py diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 351e9104..4b6a6fd1 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -171,6 +171,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: return result detections = detections[detections.confidence >= self.high_conf_det_threshold] + copied_detections = deepcopy(detections) updated_detections: list[ diff --git a/trackers/core/sort/kalman.py b/trackers/core/sort/kalman.py deleted file mode 100644 index 144fde58..00000000 --- a/trackers/core/sort/kalman.py +++ /dev/null @@ -1,147 +0,0 @@ -# ------------------------------------------------------------------------ -# Trackers -# Copyright (c) 2026 Roboflow. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ - -import numpy as np -from numpy.typing import NDArray - - -class SORTKalmanBoxTracker: - """ - The `SORTKalmanBoxTracker` class represents the internals of a single - tracked object (bounding box), with a Kalman filter to predict and update - its position. - - Attributes: - tracker_id: Unique identifier for the tracker. - number_of_successful_updates: Number of times the object has been - updated successfully. - time_since_update: Number of frames since the last update. - state: State vector of the bounding box. - F: State transition matrix. - H: Measurement matrix. - Q: Process noise covariance matrix. - R: Measurement noise covariance matrix. - P: Error covariance matrix. - count_id: Class variable to assign unique IDs to each tracker. - - Args: - bbox: Initial bounding box in the form [x1, y1, x2, y2]. - """ - - count_id: int = 0 - state: NDArray[np.float32] - F: NDArray[np.float32] - H: NDArray[np.float32] - Q: NDArray[np.float32] - R: NDArray[np.float32] - P: NDArray[np.float32] - - @classmethod - def get_next_tracker_id(cls) -> int: - next_id = cls.count_id - cls.count_id += 1 - return next_id - - def __init__(self, bbox: NDArray[np.float64]) -> None: - # Initialize with a temporary ID of -1 - # Will be assigned a real ID when the track is considered mature - self.tracker_id = -1 - - # Number of hits indicates how many times the object has been - # updated successfully - self.number_of_successful_updates = 1 - # Number of frames since the last update - self.time_since_update = 0 - - # For simplicity, we keep a small state vector: - # (x, y, x2, y2, vx, vy, vx2, vy2). - # We'll store the bounding box in "self.state" - self.state = np.zeros((8, 1), dtype=np.float32) - - # Initialize state directly from the first detection - bbox_float: NDArray[np.float32] = bbox.astype(np.float32) - self.state[0, 0] = bbox_float[0] - self.state[1, 0] = bbox_float[1] - self.state[2, 0] = bbox_float[2] - self.state[3, 0] = bbox_float[3] - - # Basic constant velocity model - self._initialize_kalman_filter() - - def _initialize_kalman_filter(self) -> None: - """ - Sets up the matrices for the Kalman filter. - """ - # State transition matrix (F): 8x8 - # We assume a constant velocity model. Positions are incremented by - # velocity each step. - self.F = np.eye(8, dtype=np.float32) - for i in range(4): - self.F[i, i + 4] = 1.0 - - # Measurement matrix (H): we directly measure x1, y1, x2, y2 - self.H = np.eye(4, 8, dtype=np.float32) # 4x8 - - # Process covariance matrix (Q) - self.Q = np.eye(8, dtype=np.float32) * 0.01 - - # Measurement covariance (R): noise in detection - self.R = np.eye(4, dtype=np.float32) * 0.1 - - # Error covariance matrix (P) - self.P = np.eye(8, dtype=np.float32) - - def predict(self) -> None: - """ - Predict the next state of the bounding box (applies the state transition). - """ - # Predict state - self.state = (self.F @ self.state).astype(np.float32) - # Predict error covariance - self.P = (self.F @ self.P @ self.F.T + self.Q).astype(np.float32) - - # Increase time since update - self.time_since_update += 1 - - def update(self, bbox: NDArray[np.float64]) -> None: - """ - Updates the state with a new detected bounding box. - - Args: - bbox: Detected bounding box in the form [x1, y1, x2, y2]. - """ - self.time_since_update = 0 - self.number_of_successful_updates += 1 - - # Kalman Gain - S: NDArray[np.float32] = (self.H @ self.P @ self.H.T + self.R).astype( - np.float32 - ) - K: NDArray[np.float32] = (self.P @ self.H.T @ np.linalg.inv(S)).astype( - np.float32 - ) - - # Residual - measurement: NDArray[np.float32] = bbox.reshape((4, 1)).astype(np.float32) - y: NDArray[np.float32] = ( - measurement - self.H @ self.state - ) # y should be float32 (4,1) - - # Update state - self.state = (self.state + K @ y).astype(np.float32) - - # Update covariance - identity_matrix: NDArray[np.float32] = np.eye(8, dtype=np.float32) - self.P = ((identity_matrix - K @ self.H) @ self.P).astype(np.float32) - - def get_state_bbox(self) -> NDArray[np.float32]: - """ - Returns the current bounding box estimate from the state vector. - - Returns: - The bounding box [x1, y1, x2, y2]. - """ - return self.state[:4, 0].flatten().astype(np.float32) From 1908f32361b1668ec6a8c74eb2f441c310ef0a2c Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 26 Feb 2026 14:41:32 -0300 Subject: [PATCH 63/87] changes from merge --- trackers/core/sort/tracker.py | 11 ++++++----- trackers/core/sort/tracklet.py | 13 ++++++++++--- trackers/core/sort/utils.py | 10 ++++++---- trackers/utils/base_tracklet.py | 9 ++++++++- 4 files changed, 30 insertions(+), 13 deletions(-) diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index 65952ef2..9377b24d 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -9,12 +9,12 @@ from scipy.optimize import linear_sum_assignment from trackers.core.base import BaseTracker +from trackers.core.sort.tracklet import SORTTracklet from trackers.core.sort.utils import ( get_alive_tracklets, get_iou_matrix, update_detections_with_track_ids, ) -from trackers.core.sort.tracklet import SORTTracklet class SORTTracker(BaseTracker): @@ -127,8 +127,9 @@ def _spawn_new_trackers( or detections.confidence[detection_idx] >= self.track_activation_threshold ): - new_tracker = SORTTracklet(detection_boxes[detection_idx], - ) + new_tracker = SORTTracklet( + detection_boxes[detection_idx], + ) self.trackers.append(new_tracker) def update(self, detections: sv.Detections) -> sv.Detections: @@ -163,8 +164,8 @@ def update(self, detections: sv.Detections) -> sv.Detections: iou_matrix = get_iou_matrix(self.trackers, detection_boxes) # Associate detections to trackers based on IOU - matched_indices, unmatched_trackers, unmatched_detections = self._get_associated_indices( - iou_matrix, detection_boxes + matched_indices, unmatched_trackers, unmatched_detections = ( + self._get_associated_indices(iou_matrix, detection_boxes) ) # Update matched trackers with assigned detections diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index 909fb42f..6fe4a097 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -1,9 +1,17 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +import numpy as np + +from trackers.utils.base_tracklet import BaseTracklet from trackers.utils.state_representations import ( BaseStateEstimator, XCYCSRStateEstimator, ) -import numpy as np -from trackers.utils.base_tracklet import BaseTracklet + class SORTTracklet(BaseTracklet): count_id: int = 0 @@ -35,4 +43,3 @@ def predict(self) -> np.ndarray: def get_state_bbox(self) -> np.ndarray: """Get current bounding box estimate from the filter/state.""" return self.kalman_filter.state_to_bbox() - diff --git a/trackers/core/sort/utils.py b/trackers/core/sort/utils.py index 81352291..0176f0d6 100644 --- a/trackers/core/sort/utils.py +++ b/trackers/core/sort/utils.py @@ -12,6 +12,7 @@ from trackers.utils.base_tracklet import BaseTracklet + def get_alive_tracklets( tracklets: Sequence[BaseTracklet], minimum_consecutive_frames: int, @@ -34,7 +35,10 @@ def get_alive_tracklets( """ alive_tracklets = [] for tracklet in tracklets: - is_mature = tracklet.number_of_successful_consecutive_updates >= minimum_consecutive_frames + is_mature = ( + tracklet.number_of_successful_consecutive_updates + >= minimum_consecutive_frames + ) is_active = tracklet.time_since_update == 0 if tracklet.time_since_update < maximum_frames_without_update and ( is_mature or is_active @@ -131,9 +135,7 @@ def update_detections_with_track_ids( ): # If tracker is mature but still has ID -1, assign a new ID if tracker_obj.tracker_id == -1: - tracker_obj.tracker_id = ( - type(tracker_obj).get_next_tracker_id() - ) + tracker_obj.tracker_id = type(tracker_obj).get_next_tracker_id() final_tracker_ids[int(col)] = tracker_obj.tracker_id used_rows.add(int(row)) used_cols.add(int(col)) diff --git a/trackers/utils/base_tracklet.py b/trackers/utils/base_tracklet.py index 392688f5..9002d4da 100644 --- a/trackers/utils/base_tracklet.py +++ b/trackers/utils/base_tracklet.py @@ -5,16 +5,23 @@ # ------------------------------------------------------------------------ from abc import ABC, abstractmethod + import numpy as np + from trackers.utils.state_representations import BaseStateEstimator + + class BaseTracklet(ABC): """ Abstract base class for all tracker-specific tracklets. Provides common interface and attributes for tracklet management. """ + count_id: int = 0 - def __init__(self, bbox: np.ndarray, state_estimator_class: type[BaseStateEstimator] ) -> None: + def __init__( + self, bbox: np.ndarray, state_estimator_class: type[BaseStateEstimator] + ) -> None: self.age = 0 self.kalman_filter: BaseStateEstimator = state_estimator_class(bbox) From 5704ce4127d439ad6a75ffb1e4c43c33f2be7472 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 3 Mar 2026 20:22:19 -0300 Subject: [PATCH 64/87] version with tracklets ready to test, bytetrack values might change because we changed kalman filter it uses and now the initialization is aligned to the original implementation --- trackers/core/bytetrack/tracker.py | 38 +++++++++++------ trackers/core/bytetrack/tracklet.py | 57 +++++++++++++++++++++++++ trackers/core/ocsort/tracker.py | 17 +++++--- trackers/core/ocsort/tracklet.py | 20 +++++++++ trackers/core/sort/tracker.py | 17 +++++++- trackers/core/sort/tracklet.py | 20 +++++++++ trackers/utils/state_representations.py | 33 ++++++++------ 7 files changed, 170 insertions(+), 32 deletions(-) create mode 100644 trackers/core/bytetrack/tracklet.py diff --git a/trackers/core/bytetrack/tracker.py b/trackers/core/bytetrack/tracker.py index 1db9231f..1a360e71 100644 --- a/trackers/core/bytetrack/tracker.py +++ b/trackers/core/bytetrack/tracker.py @@ -9,11 +9,15 @@ from scipy.optimize import linear_sum_assignment from trackers.core.base import BaseTracker -from trackers.core.bytetrack.kalman import ByteTrackKalmanBoxTracker +from trackers.core.bytetrack.tracklet import ByteTrackTracklet from trackers.core.sort.utils import ( get_alive_tracklets, get_iou_matrix, ) +from trackers.utils.state_representations import ( + BaseStateEstimator, + XYXYStateEstimator, +) class ByteTrackTracker(BaseTracker): @@ -53,6 +57,9 @@ class ByteTrackTracker(BaseTracker): detections to existing tracks. Higher values require more overlap. high_conf_det_threshold: `float` specifying threshold for separating high and low confidence detections in the two-stage association. + state_estimator_class: State estimator class to use for Kalman filter. + Defaults to `XYXYStateEstimator`. Can also use + `XCYCSRStateEstimator` for center-based representation. """ tracker_id = "bytetrack" @@ -65,6 +72,7 @@ def __init__( minimum_consecutive_frames: int = 2, minimum_iou_threshold: float = 0.1, high_conf_det_threshold: float = 0.6, + state_estimator_class: type[BaseStateEstimator] = XYXYStateEstimator, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -74,7 +82,8 @@ def __init__( self.minimum_iou_threshold = minimum_iou_threshold self.track_activation_threshold = track_activation_threshold self.high_conf_det_threshold = high_conf_det_threshold - self.tracks: list[ByteTrackKalmanBoxTracker] = [] + self.tracks: list[ByteTrackTracklet] = [] + self.state_estimator_class = state_estimator_class def update( self, @@ -129,10 +138,11 @@ def update( track = self.tracks[row] track.update(high_boxes[col]) if ( - track.number_of_successful_updates >= self.minimum_consecutive_frames + track.number_of_successful_consecutive_updates + >= self.minimum_consecutive_frames and track.tracker_id == -1 ): - track.tracker_id = ByteTrackKalmanBoxTracker.get_next_tracker_id() + track.tracker_id = ByteTrackTracklet.get_next_tracker_id() out_det_indices.append(int(high_indices[col])) out_tracker_ids.append(track.tracker_id) @@ -148,10 +158,11 @@ def update( track = remaining_tracks[row] track.update(low_boxes[col]) if ( - track.number_of_successful_updates >= self.minimum_consecutive_frames + track.number_of_successful_consecutive_updates + >= self.minimum_consecutive_frames and track.tracker_id == -1 ): - track.tracker_id = ByteTrackKalmanBoxTracker.get_next_tracker_id() + track.tracker_id = ByteTrackTracklet.get_next_tracker_id() out_det_indices.append(int(low_indices[col])) out_tracker_ids.append(track.tracker_id) @@ -161,7 +172,7 @@ def update( out_tracker_ids.append(-1) # Spawn new tracks from unmatched high-confidence detections - self._spawn_new_trackers( + self._spawn_new_tracks( detection_boxes, confidences, unmatched_high, @@ -170,10 +181,10 @@ def update( out_tracker_ids, ) - self.tracks = get_alive_trackers( - trackers=self.tracks, - maximum_frames_without_update=self.maximum_frames_without_update, + self.tracks = get_alive_tracklets( # type: ignore[assignment] + tracklets=self.tracks, minimum_consecutive_frames=self.minimum_consecutive_frames, + maximum_frames_without_update=self.maximum_frames_without_update, ) # Build final sv.Detections from original by indexing @@ -237,7 +248,10 @@ def _spawn_new_tracks( conf = float(confidences[global_idx]) if conf >= self.track_activation_threshold: self.tracks.append( - ByteTrackKalmanBoxTracker(bbox=detection_boxes[global_idx]) + ByteTrackTracklet( + initial_bbox=detection_boxes[global_idx], + state_estimator_class=self.state_estimator_class, + ) ) out_det_indices.append(global_idx) out_tracker_ids.append(-1) @@ -247,4 +261,4 @@ def reset(self) -> None: Call this method when switching to a new video or scene. """ self.tracks = [] - ByteTrackKalmanBoxTracker.count_id = 0 + ByteTrackTracklet.count_id = 0 diff --git a/trackers/core/bytetrack/tracklet.py b/trackers/core/bytetrack/tracklet.py new file mode 100644 index 00000000..0755ccb3 --- /dev/null +++ b/trackers/core/bytetrack/tracklet.py @@ -0,0 +1,57 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +import numpy as np + +from trackers.utils.base_tracklet import BaseTracklet +from trackers.utils.state_representations import ( + BaseStateEstimator, + XYXYStateEstimator, +) + + +class ByteTrackTracklet(BaseTracklet): + count_id: int = 0 + + def __init__( + self, + initial_bbox: np.ndarray, + state_estimator_class: type[BaseStateEstimator] = XYXYStateEstimator, + ) -> None: + super().__init__(initial_bbox, state_estimator_class) + self._configure_noise() + # Count initial bbox as first successful update (matches original + # ByteTrackKalmanBoxTracker behavior where hits started at 1) + self.number_of_successful_consecutive_updates = 1 + + def update(self, bbox: np.ndarray | None) -> None: + """Update tracklet with new observation or None if missed.""" + if bbox is not None: + self.kalman_filter.update(bbox) + self.time_since_update = 0 + self.number_of_successful_consecutive_updates += 1 + else: + self.kalman_filter.update(None) + self.time_since_update += 1 + self.number_of_successful_consecutive_updates = 0 + + def predict(self) -> np.ndarray: + """Predict next bounding box position.""" + self.kalman_filter.predict() + self.age += 1 + return self.kalman_filter.state_to_bbox() + + def get_state_bbox(self) -> np.ndarray: + """Get current bounding box estimate from the filter/state.""" + return self.kalman_filter.state_to_bbox() + + def _configure_noise(self) -> None: + """Configure Kalman filter noise (original ByteTrack tuning).""" + kf = self.kalman_filter.kf + self.kalman_filter.set_kf_covariances( + R=kf.R * 0.1, + Q=kf.Q * 0.01, + ) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 8c666e6e..99d487c2 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -4,6 +4,8 @@ # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ +from copy import deepcopy + import numpy as np import supervision as sv from scipy.optimize import linear_sum_assignment @@ -14,7 +16,10 @@ _build_direction_consistency_matrix_batch, _get_iou_matrix, ) -from trackers.utils.state_representations import XCYCSRStateEstimator +from trackers.utils.state_representations import ( + BaseStateEstimator, + XCYCSRStateEstimator, +) class OCSORTTracker(BaseTracker): @@ -57,6 +62,9 @@ class OCSORTTracker(BaseTracker): delta_t: `int` specifying number of past frames to use for velocity estimation. Higher values provide more stable direction estimates during occlusion. + state_estimator_class: State estimator class to use for Kalman filter. + Defaults to `XCYCSRStateEstimator`. Can also use + `XYXYStateEstimator` for corner-based representation. """ tracker_id = "ocsort" @@ -70,6 +78,7 @@ def __init__( direction_consistency_weight: float = 0.2, high_conf_det_threshold: float = 0.6, delta_t: int = 3, + state_estimator_class: type[BaseStateEstimator] = XCYCSRStateEstimator, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -164,8 +173,6 @@ def update(self, detections: sv.Detections) -> sv.Detections: return result detections = detections[detections.confidence >= self.high_conf_det_threshold] - - copied_detections = deepcopy(detections) detection_boxes = detections.xyxy if len(detections) > 0 else np.empty((0, 4)) confidences = ( @@ -232,7 +239,6 @@ def update(self, detections: sv.Detections) -> sv.Detections: self.tracks = self._prune_expired_tracklets() - remaining_indices = [unmatched_detections[i] for i in ocr_unmatched_dets] self._spawn_new_tracklets(detection_boxes[remaining_indices]) for det_idx in remaining_indices: @@ -251,7 +257,8 @@ def update(self, detections: sv.Detections) -> sv.Detections: # Build output — single index into the filtered detections preserves # all metadata (confidence, class_id, mask, data dict). if out_det_indices: - result = detections[out_det_indices] + copied_detections = deepcopy(detections) + result = copied_detections[out_det_indices] result.tracker_id = np.array(out_tracker_ids, dtype=int) else: result = sv.Detections.empty() diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 29758e8a..aac00d1b 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -61,6 +61,7 @@ def __init__( # Initialize state estimator (wraps KalmanFilter + state repr) super().__init__(initial_bbox, state_estimator_class) + self._configure_noise() # Observation history for ORU and delta_t self.delta_t = delta_t self.last_observation = initial_bbox @@ -261,6 +262,25 @@ def get_state_bbox(self) -> np.ndarray: """ return self.kalman_filter.state_to_bbox() + def _configure_noise(self) -> None: + """Configure Kalman filter noise matrices (OC-SORT paper tuning).""" + kf = self.kalman_filter.kf + R = kf.R + P = kf.P + Q = kf.Q + if isinstance(self.kalman_filter, XCYCSRStateEstimator): + R[2:, 2:] *= 10.0 + P[4:, 4:] *= 1000.0 + P *= 10.0 + Q[-1, -1] *= 0.01 + Q[4:, 4:] *= 0.01 + else: + # XYXY: same velocity uncertainty scaling + P[4:, 4:] *= 1000.0 + P *= 10.0 + Q[4:, 4:] *= 0.01 + self.kalman_filter.set_kf_covariances(R=R, Q=Q, P=P) + def resolve_tracker_id( self, minimum_consecutive_frames: int, diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index 9f2379fb..f0987d19 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -14,6 +14,10 @@ get_alive_tracklets, get_iou_matrix, ) +from trackers.utils.state_representations import ( + BaseStateEstimator, + XCYCSRStateEstimator, +) class SORTTracker(BaseTracker): @@ -51,6 +55,9 @@ class SORTTracker(BaseTracker): threshold, tracks are assigned `tracker_id` of `-1`. minimum_iou_threshold: `float` specifying IoU threshold for associating detections to existing tracks. Higher values require more overlap. + state_estimator_class: State estimator class to use for Kalman filter. + Defaults to `XCYCSRStateEstimator`. Can also use + `XYXYStateEstimator` for corner-based representation. """ tracker_id = "sort" @@ -62,6 +69,7 @@ def __init__( track_activation_threshold: float = 0.25, minimum_consecutive_frames: int = 3, minimum_iou_threshold: float = 0.3, + state_estimator_class: type[BaseStateEstimator] = XCYCSRStateEstimator, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -70,6 +78,7 @@ def __init__( self.minimum_consecutive_frames = minimum_consecutive_frames self.minimum_iou_threshold = minimum_iou_threshold self.track_activation_threshold = track_activation_threshold + self.state_estimator_class = state_estimator_class # Active trackers self.trackers: list[SORTTracklet] = [] @@ -119,6 +128,7 @@ def _spawn_new_trackers( ): new_tracker = SORTTracklet( detection_boxes[detection_idx], + state_estimator_class=self.state_estimator_class, ) self.trackers.append(new_tracker) @@ -166,7 +176,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: self._spawn_new_trackers(detections, detection_boxes, unmatched_detections) # Remove dead trackers - self.trackers = get_alive_tracklets( + self.trackers = get_alive_tracklets( # type: ignore[assignment] self.trackers, self.minimum_consecutive_frames, self.maximum_frames_without_update, @@ -175,7 +185,10 @@ def update(self, detections: sv.Detections) -> sv.Detections: # Build tracker_ids from the recorded mapping (no deepcopy, no re-IoU) tracker_ids = np.full(len(detection_boxes), -1, dtype=int) for det_idx, tracker in matched_tracker_for_det.items(): - if tracker.number_of_successful_updates >= self.minimum_consecutive_frames: + if ( + tracker.number_of_successful_consecutive_updates + >= self.minimum_consecutive_frames + ): if tracker.tracker_id == -1: tracker.tracker_id = SORTTracklet.get_next_tracker_id() tracker_ids[det_idx] = tracker.tracker_id diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index 6fe4a097..33700f6c 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -22,6 +22,7 @@ def __init__( state_estimator_class: type[BaseStateEstimator] = XCYCSRStateEstimator, ) -> None: super().__init__(initial_bbox, state_estimator_class) + self._configure_noise() def update(self, bbox: np.ndarray | None) -> None: """Update tracklet with new observation or None if missed.""" @@ -43,3 +44,22 @@ def predict(self) -> np.ndarray: def get_state_bbox(self) -> np.ndarray: """Get current bounding box estimate from the filter/state.""" return self.kalman_filter.state_to_bbox() + + def _configure_noise(self) -> None: + """Configure Kalman filter noise matrices (OC-SORT paper tuning).""" + kf = self.kalman_filter.kf + R = kf.R + P = kf.P + Q = kf.Q + if isinstance(self.kalman_filter, XCYCSRStateEstimator): + R[2:, 2:] *= 10.0 + P[4:, 4:] *= 1000.0 + P *= 10.0 + Q[-1, -1] *= 0.01 + Q[4:, 4:] *= 0.01 + else: + # XYXY: same velocity uncertainty scaling + P[4:, 4:] *= 1000.0 + P *= 10.0 + Q[4:, 4:] *= 0.01 + self.kalman_filter.set_kf_covariances(R=R, Q=Q, P=P) diff --git a/trackers/utils/state_representations.py b/trackers/utils/state_representations.py index f1e93a22..17b878b5 100644 --- a/trackers/utils/state_representations.py +++ b/trackers/utils/state_representations.py @@ -126,6 +126,26 @@ def set_state(self, state: dict) -> None: """ self.kf.set_state(state) + def set_kf_covariances( + self, + R: np.ndarray | None = None, + Q: np.ndarray | None = None, + P: np.ndarray | None = None, + ) -> None: + """Set Kalman filter parameters. + + Args: + R: Measurement noise covariance matrix. + Q: Process noise covariance matrix. + P: Error covariance matrix. + """ + if R is not None: + self.kf.R = R + if Q is not None: + self.kf.Q = Q + if P is not None: + self.kf.P = P + class XCYCSRStateEstimator(BaseStateEstimator): """Center-based Kalman filter with 7 state dimensions and 4 measurements. @@ -157,13 +177,6 @@ def _create_filter(self, bbox: np.ndarray) -> KalmanFilter: # Measurement function: observe (x, y, s, r) from state kf.H = np.eye(4, 7, dtype=np.float64) - # Noise tuning (from OC-SORT paper) - kf.R[2:, 2:] *= 10.0 - kf.P[4:, 4:] *= 1000.0 # high uncertainty for velocities - kf.P *= 10.0 - kf.Q[-1, -1] *= 0.01 - kf.Q[4:, 4:] *= 0.01 - # Initialise state with first observation kf.x[:4] = xyxy_to_xcycsr(bbox).reshape((4, 1)) @@ -211,12 +224,6 @@ def _create_filter(self, bbox: np.ndarray) -> KalmanFilter: # Measurement function: observe (x1, y1, x2, y2) from state kf.H = np.eye(4, 8, dtype=np.float64) - # Noise tuning (similar scaling to XCYCSR version) - kf.R *= 1.0 # measurement noise - kf.P[4:, 4:] *= 1000.0 # high uncertainty for velocities - kf.P *= 10.0 - kf.Q[4:, 4:] *= 0.01 - # Initialise state with first observation (direct XYXY) kf.x[:4] = bbox.reshape((4, 1)) From 8a78611d5cff69f4ccedf37cf4568c5407ccf8d7 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 4 Mar 2026 15:02:15 -0300 Subject: [PATCH 65/87] small bug fix in sort, passed detections instead of confidence --- trackers/core/sort/tracker.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index f0987d19..6f5c84dd 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -173,7 +173,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: # Update non matched for increasing time_since_update for index in unmatched_trackers: self.trackers[index].update(None) - self._spawn_new_trackers(detections, detection_boxes, unmatched_detections) + self._spawn_new_trackers(detections.confidence, detection_boxes, unmatched_detections) # Remove dead trackers self.trackers = get_alive_tracklets( # type: ignore[assignment] From 8f9cc44bfdb769c121e1a353a10c6b2b686b4fd2 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 4 Mar 2026 15:36:09 -0300 Subject: [PATCH 66/87] trying oc-sort style initialization in bytetrack --- trackers/core/bytetrack/tracklet.py | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/trackers/core/bytetrack/tracklet.py b/trackers/core/bytetrack/tracklet.py index 0755ccb3..39e75f78 100644 --- a/trackers/core/bytetrack/tracklet.py +++ b/trackers/core/bytetrack/tracklet.py @@ -9,6 +9,7 @@ from trackers.utils.base_tracklet import BaseTracklet from trackers.utils.state_representations import ( BaseStateEstimator, + XCYCSRStateEstimator, XYXYStateEstimator, ) @@ -51,7 +52,22 @@ def get_state_bbox(self) -> np.ndarray: def _configure_noise(self) -> None: """Configure Kalman filter noise (original ByteTrack tuning).""" kf = self.kalman_filter.kf - self.kalman_filter.set_kf_covariances( - R=kf.R * 0.1, - Q=kf.Q * 0.01, - ) + # self.kalman_filter.set_kf_covariances( + # R=kf.R * 0.1, + # Q=kf.Q * 0.01, + # ) + R = kf.R + P = kf.P + Q = kf.Q + if isinstance(self.kalman_filter, XCYCSRStateEstimator): + R[2:, 2:] *= 10.0 + P[4:, 4:] *= 1000.0 + P *= 10.0 + Q[-1, -1] *= 0.01 + Q[4:, 4:] *= 0.01 + else: + # XYXY: same velocity uncertainty scaling + P[4:, 4:] *= 1000.0 + P *= 10.0 + Q[4:, 4:] *= 0.01 + self.kalman_filter.set_kf_covariances(R=R, Q=Q, P=P) From b33f3d157a7eda7ca1bf2089b877dace6835b435 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 4 Mar 2026 15:42:56 -0300 Subject: [PATCH 67/87] rollback trying oc-sort style initialization in bytetrack, now it uses the original one from byte --- trackers/core/bytetrack/tracklet.py | 24 ++++-------------------- 1 file changed, 4 insertions(+), 20 deletions(-) diff --git a/trackers/core/bytetrack/tracklet.py b/trackers/core/bytetrack/tracklet.py index 39e75f78..0755ccb3 100644 --- a/trackers/core/bytetrack/tracklet.py +++ b/trackers/core/bytetrack/tracklet.py @@ -9,7 +9,6 @@ from trackers.utils.base_tracklet import BaseTracklet from trackers.utils.state_representations import ( BaseStateEstimator, - XCYCSRStateEstimator, XYXYStateEstimator, ) @@ -52,22 +51,7 @@ def get_state_bbox(self) -> np.ndarray: def _configure_noise(self) -> None: """Configure Kalman filter noise (original ByteTrack tuning).""" kf = self.kalman_filter.kf - # self.kalman_filter.set_kf_covariances( - # R=kf.R * 0.1, - # Q=kf.Q * 0.01, - # ) - R = kf.R - P = kf.P - Q = kf.Q - if isinstance(self.kalman_filter, XCYCSRStateEstimator): - R[2:, 2:] *= 10.0 - P[4:, 4:] *= 1000.0 - P *= 10.0 - Q[-1, -1] *= 0.01 - Q[4:, 4:] *= 0.01 - else: - # XYXY: same velocity uncertainty scaling - P[4:, 4:] *= 1000.0 - P *= 10.0 - Q[4:, 4:] *= 0.01 - self.kalman_filter.set_kf_covariances(R=R, Q=Q, P=P) + self.kalman_filter.set_kf_covariances( + R=kf.R * 0.1, + Q=kf.Q * 0.01, + ) From bb3199e099a753d467f639ade96299aacfe7bf5e Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 5 Mar 2026 12:21:30 -0300 Subject: [PATCH 68/87] aligned format --- trackers/core/bytetrack/tracker.py | 11 ++--- trackers/core/ocsort/tracker.py | 2 +- trackers/core/sort/tracker.py | 68 +++++++++++++++--------------- trackers/core/sort/tracklet.py | 2 +- trackers/core/sort/utils.py | 10 ++--- 5 files changed, 45 insertions(+), 48 deletions(-) diff --git a/trackers/core/bytetrack/tracker.py b/trackers/core/bytetrack/tracker.py index 1a360e71..9cff7617 100644 --- a/trackers/core/bytetrack/tracker.py +++ b/trackers/core/bytetrack/tracker.py @@ -10,10 +10,7 @@ from trackers.core.base import BaseTracker from trackers.core.bytetrack.tracklet import ByteTrackTracklet -from trackers.core.sort.utils import ( - get_alive_tracklets, - get_iou_matrix, -) +from trackers.core.sort.utils import _get_alive_tracklets, _get_iou_matrix from trackers.utils.state_representations import ( BaseStateEstimator, XYXYStateEstimator, @@ -129,7 +126,7 @@ def update( low_boxes = detection_boxes[low_indices] # Step 1: associate high-confidence detections to all tracks - iou_matrix = get_iou_matrix(self.tracks, high_boxes) + iou_matrix = _get_iou_matrix(self.tracks, high_boxes) matched, unmatched_tracks, unmatched_high = self._get_associated_indices( iou_matrix, self.minimum_iou_threshold ) @@ -149,7 +146,7 @@ def update( remaining_tracks = [self.tracks[i] for i in unmatched_tracks] # Step 2: associate low-confidence detections to remaining tracks - iou_matrix = get_iou_matrix(remaining_tracks, low_boxes) + iou_matrix = _get_iou_matrix(remaining_tracks, low_boxes) matched, _, unmatched_low = self._get_associated_indices( iou_matrix, self.minimum_iou_threshold ) @@ -181,7 +178,7 @@ def update( out_tracker_ids, ) - self.tracks = get_alive_tracklets( # type: ignore[assignment] + self.tracks = _get_alive_tracklets( # type: ignore[assignment] tracklets=self.tracks, minimum_consecutive_frames=self.minimum_consecutive_frames, maximum_frames_without_update=self.maximum_frames_without_update, diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index 99d487c2..94b66305 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -92,7 +92,7 @@ def __init__( self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 - self.state_estimator_class = XCYCSRStateEstimator + self.state_estimator_class = state_estimator_class def _get_associated_indices( self, diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index 6f5c84dd..723454f1 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -11,8 +11,8 @@ from trackers.core.base import BaseTracker from trackers.core.sort.tracklet import SORTTracklet from trackers.core.sort.utils import ( - get_alive_tracklets, - get_iou_matrix, + _get_alive_tracklets, + _get_iou_matrix, ) from trackers.utils.state_representations import ( BaseStateEstimator, @@ -80,27 +80,27 @@ def __init__( self.track_activation_threshold = track_activation_threshold self.state_estimator_class = state_estimator_class - # Active trackers - self.trackers: list[SORTTracklet] = [] + # Active tracklets + self.tracklets: list[SORTTracklet] = [] def _get_associated_indices( self, iou_matrix: np.ndarray, detection_boxes: np.ndarray ) -> tuple[list[tuple[int, int]], set[int], set[int]]: """ - Associate detections to trackers based on IOU + Associate detections to tracklets based on IOU Args: iou_matrix: IOU cost matrix. detection_boxes: Detected bounding boxes in the form [x1, y1, x2, y2]. Returns: - Matched indices, unmatched trackers, unmatched detections. + Matched indices, unmatched tracklets, unmatched detections. """ matched_indices = [] - unmatched_trackers = set(range(len(self.trackers))) + unmatched_tracklets = set(range(len(self.tracklets))) unmatched_detections = set(range(len(detection_boxes))) - if len(self.trackers) > 0 and len(detection_boxes) > 0: + if len(self.tracklets) > 0 and len(detection_boxes) > 0: # Find optimal assignment using scipy.optimize.linear_sum_assignment. # Note that it uses a a modified Jonker-Volgenant algorithm with no # initialization instead of the Hungarian algorithm as mentioned in the @@ -109,12 +109,12 @@ def _get_associated_indices( for row, col in zip(row_indices, col_indices): if iou_matrix[row, col] >= self.minimum_iou_threshold: matched_indices.append((row, col)) - unmatched_trackers.remove(row) + unmatched_tracklets.remove(row) unmatched_detections.remove(col) - return matched_indices, unmatched_trackers, unmatched_detections + return matched_indices, unmatched_tracklets, unmatched_detections - def _spawn_new_trackers( + def _spawn_new_tracklets( self, confidences: np.ndarray | None, detection_boxes: np.ndarray, @@ -130,7 +130,7 @@ def _spawn_new_trackers( detection_boxes[detection_idx], state_estimator_class=self.state_estimator_class, ) - self.trackers.append(new_tracker) + self.tracklets.append(new_tracker) def update(self, detections: sv.Detections) -> sv.Detections: """Update tracker state with new detections and return tracked objects. @@ -146,7 +146,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: `sv.Detections` with `tracker_id` assigned for each detection. Unmatched or immature tracks have `tracker_id` of `-1`. """ - if len(self.trackers) == 0 and len(detections) == 0: + if len(self.tracklets) == 0 and len(detections) == 0: detections.tracker_id = np.array([], dtype=int) return detections @@ -154,44 +154,46 @@ def update(self, detections: sv.Detections) -> sv.Detections: detections.xyxy if len(detections) > 0 else np.array([]).reshape(0, 4) ) - for tracker in self.trackers: + for tracker in self.tracklets: tracker.predict() - iou_matrix = get_iou_matrix(self.trackers, detection_boxes) + iou_matrix = _get_iou_matrix(self.tracklets, detection_boxes) - # Associate detections to trackers based on IOU - matched_indices, unmatched_trackers, unmatched_detections = ( + # Associate detections to tracklets based on IOU + matched_indices, unmatched_tracklets, unmatched_detections = ( self._get_associated_indices(iou_matrix, detection_boxes) ) - # Update matched trackers and record the det_idx -> tracker mapping - matched_tracker_for_det: dict[int, SORTTracklet] = {} + # Update matched tracklets and record the det_idx -> tracklet mapping + matched_tracklet_for_det: dict[int, SORTTracklet] = {} for row, col in matched_indices: - self.trackers[row].update(detection_boxes[col]) - matched_tracker_for_det[col] = self.trackers[row] + self.tracklets[row].update(detection_boxes[col]) + matched_tracklet_for_det[col] = self.tracklets[row] # Update non matched for increasing time_since_update - for index in unmatched_trackers: - self.trackers[index].update(None) - self._spawn_new_trackers(detections.confidence, detection_boxes, unmatched_detections) + for index in unmatched_tracklets: + self.tracklets[index].update(None) + self._spawn_new_tracklets( + detections.confidence, detection_boxes, unmatched_detections + ) - # Remove dead trackers - self.trackers = get_alive_tracklets( # type: ignore[assignment] - self.trackers, + # Remove dead tracklets + self.tracklets = _get_alive_tracklets( # type: ignore[assignment] + self.tracklets, self.minimum_consecutive_frames, self.maximum_frames_without_update, ) # Build tracker_ids from the recorded mapping (no deepcopy, no re-IoU) tracker_ids = np.full(len(detection_boxes), -1, dtype=int) - for det_idx, tracker in matched_tracker_for_det.items(): + for det_idx, tracklet in matched_tracklet_for_det.items(): if ( - tracker.number_of_successful_consecutive_updates + tracklet.number_of_successful_consecutive_updates >= self.minimum_consecutive_frames ): - if tracker.tracker_id == -1: - tracker.tracker_id = SORTTracklet.get_next_tracker_id() - tracker_ids[det_idx] = tracker.tracker_id + if tracklet.tracker_id == -1: + tracklet.tracker_id = SORTTracklet.get_next_tracker_id() + tracker_ids[det_idx] = tracklet.tracker_id detections.tracker_id = tracker_ids return detections @@ -200,5 +202,5 @@ def reset(self) -> None: """Reset tracker state by clearing all tracks and resetting ID counter. Call this method when switching to a new video or scene. """ - self.trackers = [] + self.tracklets = [] SORTTracklet.count_id = 0 diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index 33700f6c..a095d874 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -46,7 +46,7 @@ def get_state_bbox(self) -> np.ndarray: return self.kalman_filter.state_to_bbox() def _configure_noise(self) -> None: - """Configure Kalman filter noise matrices (OC-SORT paper tuning).""" + """Configure Kalman filter noise matrices (OC-SORT paper behaviour).""" kf = self.kalman_filter.kf R = kf.R P = kf.P diff --git a/trackers/core/sort/utils.py b/trackers/core/sort/utils.py index 392d556a..057a7dc7 100644 --- a/trackers/core/sort/utils.py +++ b/trackers/core/sort/utils.py @@ -5,7 +5,6 @@ # ------------------------------------------------------------------------ from collections.abc import Sequence -from copy import deepcopy import numpy as np import supervision as sv @@ -13,7 +12,7 @@ from trackers.utils.base_tracklet import BaseTracklet -def get_alive_tracklets( +def _get_alive_tracklets( tracklets: Sequence[BaseTracklet], minimum_consecutive_frames: int, maximum_frames_without_update: int, @@ -24,7 +23,7 @@ def get_alive_tracklets( it was just updated). Args: - tracklets: List of KalmanBoxTracker objects. + tracklets: List of BaseTracklet objects. minimum_consecutive_frames: Number of consecutive frames that an object must be tracked before it is considered a 'valid' track. maximum_frames_without_update: Maximum number of frames without update @@ -47,14 +46,14 @@ def get_alive_tracklets( return alive_tracklets -def get_iou_matrix( +def _get_iou_matrix( tracks: Sequence[BaseTracklet], detection_boxes: np.ndarray ) -> np.ndarray: """ Build IOU cost matrix between detections and predicted bounding boxes Args: - tracks: List of KalmanBoxTracker objects. + tracks: List of BaseTracklet objects. detection_boxes: Detected bounding boxes in the form [x1, y1, x2, y2]. @@ -72,4 +71,3 @@ def get_iou_matrix( iou_matrix = np.zeros((len(tracks), len(detection_boxes)), dtype=np.float32) return iou_matrix - From 50e511738ef3f54bbd8e984c358e5be5a70573c4 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 10 Mar 2026 11:10:02 -0300 Subject: [PATCH 69/87] default in sort is XYXY now --- trackers/core/sort/tracker.py | 6 +++--- trackers/core/sort/tracklet.py | 3 ++- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index 723454f1..baba4739 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -16,7 +16,7 @@ ) from trackers.utils.state_representations import ( BaseStateEstimator, - XCYCSRStateEstimator, + XYXYStateEstimator, ) @@ -56,7 +56,7 @@ class SORTTracker(BaseTracker): minimum_iou_threshold: `float` specifying IoU threshold for associating detections to existing tracks. Higher values require more overlap. state_estimator_class: State estimator class to use for Kalman filter. - Defaults to `XCYCSRStateEstimator`. Can also use + Defaults to `XYXYStateEstimator`. Can also use `XYXYStateEstimator` for corner-based representation. """ @@ -69,7 +69,7 @@ def __init__( track_activation_threshold: float = 0.25, minimum_consecutive_frames: int = 3, minimum_iou_threshold: float = 0.3, - state_estimator_class: type[BaseStateEstimator] = XCYCSRStateEstimator, + state_estimator_class: type[BaseStateEstimator] = XYXYStateEstimator, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index a095d874..1d11a51a 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -10,6 +10,7 @@ from trackers.utils.state_representations import ( BaseStateEstimator, XCYCSRStateEstimator, + XYXYStateEstimator, ) @@ -19,7 +20,7 @@ class SORTTracklet(BaseTracklet): def __init__( self, initial_bbox: np.ndarray, - state_estimator_class: type[BaseStateEstimator] = XCYCSRStateEstimator, + state_estimator_class: type[BaseStateEstimator] = XYXYStateEstimator, ) -> None: super().__init__(initial_bbox, state_estimator_class) self._configure_noise() From 9a4162980158b59282e88c61589a06a07bfae627 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 10 Mar 2026 13:06:42 -0300 Subject: [PATCH 70/87] now sort tracklets initialize number_of_successful_consecutive_updates --- trackers/core/sort/tracklet.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index 1d11a51a..5a2ce2c1 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -24,6 +24,8 @@ def __init__( ) -> None: super().__init__(initial_bbox, state_estimator_class) self._configure_noise() + # SORTKalmanBoxTracker behavior where hits started at 1) + self.number_of_successful_consecutive_updates = 1 def update(self, bbox: np.ndarray | None) -> None: """Update tracklet with new observation or None if missed.""" From 6207d093ea8a12cb25774ca637c548cac24ae891 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 10 Mar 2026 13:18:08 -0300 Subject: [PATCH 71/87] initializing parameters like old version --- trackers/core/sort/tracklet.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index 5a2ce2c1..0048a43f 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -49,7 +49,8 @@ def get_state_bbox(self) -> np.ndarray: return self.kalman_filter.state_to_bbox() def _configure_noise(self) -> None: - """Configure Kalman filter noise matrices (OC-SORT paper behaviour).""" + """Configure Kalman filter noise matrices (OC-SORT paper behaviour) and SORT + behaviour for XYXY coordinates.""" kf = self.kalman_filter.kf R = kf.R P = kf.P @@ -61,8 +62,16 @@ def _configure_noise(self) -> None: Q[-1, -1] *= 0.01 Q[4:, 4:] *= 0.01 else: - # XYXY: same velocity uncertainty scaling - P[4:, 4:] *= 1000.0 - P *= 10.0 - Q[4:, 4:] *= 0.01 + + + + # Process covariance matrix (Q) + Q = np.eye(8, dtype=np.float32) * 0.01 + + # Measurement covariance (R): noise in detection + R = np.eye(4, dtype=np.float32) * 0.1 + + # Error covariance matrix (P) + P = np.eye(8, dtype=np.float32) + self.kalman_filter.set_kf_covariances(R=R, Q=Q, P=P) From 8adae832d171b821540f7035133b2d7244ef3938 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 10 Mar 2026 16:18:33 +0000 Subject: [PATCH 72/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- trackers/core/sort/tracklet.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index 0048a43f..cc61e14f 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -49,7 +49,7 @@ def get_state_bbox(self) -> np.ndarray: return self.kalman_filter.state_to_bbox() def _configure_noise(self) -> None: - """Configure Kalman filter noise matrices (OC-SORT paper behaviour) and SORT + """Configure Kalman filter noise matrices (OC-SORT paper behaviour) and SORT behaviour for XYXY coordinates.""" kf = self.kalman_filter.kf R = kf.R @@ -62,9 +62,6 @@ def _configure_noise(self) -> None: Q[-1, -1] *= 0.01 Q[4:, 4:] *= 0.01 else: - - - # Process covariance matrix (Q) Q = np.eye(8, dtype=np.float32) * 0.01 From 1428b739edafab7cbec1e96fb32cd9cb4615af05 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 10 Mar 2026 13:51:15 -0300 Subject: [PATCH 73/87] Now SORT uses number_of_succesful_updates (not consecutive) --- trackers/core/sort/tracker.py | 14 +++++--------- trackers/core/sort/tracklet.py | 7 ++----- 2 files changed, 7 insertions(+), 14 deletions(-) diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index baba4739..a1a93ec4 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -116,16 +116,12 @@ def _get_associated_indices( def _spawn_new_tracklets( self, - confidences: np.ndarray | None, + confidences: np.ndarray , detection_boxes: np.ndarray, unmatched_detections: set[int], ) -> None: for detection_idx in unmatched_detections: - if ( - confidences is None - or detection_idx >= len(confidences) - or confidences[detection_idx] >= self.track_activation_threshold - ): + if confidences[detection_idx] >= self.track_activation_threshold: new_tracker = SORTTracklet( detection_boxes[detection_idx], state_estimator_class=self.state_estimator_class, @@ -154,8 +150,8 @@ def update(self, detections: sv.Detections) -> sv.Detections: detections.xyxy if len(detections) > 0 else np.array([]).reshape(0, 4) ) - for tracker in self.tracklets: - tracker.predict() + for tracklet in self.tracklets: + tracklet.predict() iou_matrix = _get_iou_matrix(self.tracklets, detection_boxes) @@ -188,7 +184,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: tracker_ids = np.full(len(detection_boxes), -1, dtype=int) for det_idx, tracklet in matched_tracklet_for_det.items(): if ( - tracklet.number_of_successful_consecutive_updates + tracklet.number_of_successful_updates >= self.minimum_consecutive_frames ): if tracklet.tracker_id == -1: diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index 0048a43f..a0fc4e54 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -25,18 +25,17 @@ def __init__( super().__init__(initial_bbox, state_estimator_class) self._configure_noise() # SORTKalmanBoxTracker behavior where hits started at 1) - self.number_of_successful_consecutive_updates = 1 + self.number_of_successful_updates = 1 # SORT doesnt use number_of_successful_consecutive_updates def update(self, bbox: np.ndarray | None) -> None: """Update tracklet with new observation or None if missed.""" if bbox is not None: self.kalman_filter.update(bbox) self.time_since_update = 0 - self.number_of_successful_consecutive_updates += 1 + self.number_of_successful_updates += 1 else: self.kalman_filter.update(None) self.time_since_update += 1 - self.number_of_successful_consecutive_updates = 0 def predict(self) -> np.ndarray: """Predict next bounding box position.""" @@ -63,8 +62,6 @@ def _configure_noise(self) -> None: Q[4:, 4:] *= 0.01 else: - - # Process covariance matrix (Q) Q = np.eye(8, dtype=np.float32) * 0.01 From ae46434480695357abc667a8acdcc056dceadd46 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 10 Mar 2026 16:53:54 +0000 Subject: [PATCH 74/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- trackers/core/sort/tracker.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index a1a93ec4..37b93c44 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -116,12 +116,12 @@ def _get_associated_indices( def _spawn_new_tracklets( self, - confidences: np.ndarray , + confidences: np.ndarray, detection_boxes: np.ndarray, unmatched_detections: set[int], ) -> None: for detection_idx in unmatched_detections: - if confidences[detection_idx] >= self.track_activation_threshold: + if confidences[detection_idx] >= self.track_activation_threshold: new_tracker = SORTTracklet( detection_boxes[detection_idx], state_estimator_class=self.state_estimator_class, @@ -183,10 +183,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: # Build tracker_ids from the recorded mapping (no deepcopy, no re-IoU) tracker_ids = np.full(len(detection_boxes), -1, dtype=int) for det_idx, tracklet in matched_tracklet_for_det.items(): - if ( - tracklet.number_of_successful_updates - >= self.minimum_consecutive_frames - ): + if tracklet.number_of_successful_updates >= self.minimum_consecutive_frames: if tracklet.tracker_id == -1: tracklet.tracker_id = SORTTracklet.get_next_tracker_id() tracker_ids[det_idx] = tracklet.tracker_id From 9369c6b0daed6a557a734ddc65ca6b8cf9b2bbb4 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 10 Mar 2026 13:55:41 -0300 Subject: [PATCH 75/87] Now SORT uses number_of_succesful_updates (not consecutive) --- trackers/core/sort/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/trackers/core/sort/utils.py b/trackers/core/sort/utils.py index 057a7dc7..f1fd93a5 100644 --- a/trackers/core/sort/utils.py +++ b/trackers/core/sort/utils.py @@ -35,7 +35,7 @@ def _get_alive_tracklets( alive_tracklets = [] for tracklet in tracklets: is_mature = ( - tracklet.number_of_successful_consecutive_updates + tracklet.number_of_successful_updates >= minimum_consecutive_frames ) is_active = tracklet.time_since_update == 0 From c2f2daf0b87337ce998f2701abdd01a00e5ab23b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 10 Mar 2026 16:56:22 +0000 Subject: [PATCH 76/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- trackers/core/sort/utils.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/trackers/core/sort/utils.py b/trackers/core/sort/utils.py index f1fd93a5..d3b69468 100644 --- a/trackers/core/sort/utils.py +++ b/trackers/core/sort/utils.py @@ -34,10 +34,7 @@ def _get_alive_tracklets( """ alive_tracklets = [] for tracklet in tracklets: - is_mature = ( - tracklet.number_of_successful_updates - >= minimum_consecutive_frames - ) + is_mature = tracklet.number_of_successful_updates >= minimum_consecutive_frames is_active = tracklet.time_since_update == 0 if tracklet.time_since_update < maximum_frames_without_update and ( is_mature or is_active From 3c54462a843fada182fc3136a15654f6f9c85202 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Wed, 11 Mar 2026 09:47:26 -0300 Subject: [PATCH 77/87] fix bytetrack broken because of usage of sort utility --- trackers/core/bytetrack/tracker.py | 3 ++- trackers/core/bytetrack/utils.py | 43 ++++++++++++++++++++++++++++++ trackers/core/sort/utils.py | 12 ++++----- 3 files changed, 50 insertions(+), 8 deletions(-) create mode 100644 trackers/core/bytetrack/utils.py diff --git a/trackers/core/bytetrack/tracker.py b/trackers/core/bytetrack/tracker.py index 9cff7617..b374a4e9 100644 --- a/trackers/core/bytetrack/tracker.py +++ b/trackers/core/bytetrack/tracker.py @@ -10,7 +10,8 @@ from trackers.core.base import BaseTracker from trackers.core.bytetrack.tracklet import ByteTrackTracklet -from trackers.core.sort.utils import _get_alive_tracklets, _get_iou_matrix +from trackers.core.bytetrack.utils import _get_alive_tracklets +from trackers.core.sort.utils import _get_iou_matrix from trackers.utils.state_representations import ( BaseStateEstimator, XYXYStateEstimator, diff --git a/trackers/core/bytetrack/utils.py b/trackers/core/bytetrack/utils.py new file mode 100644 index 00000000..7bb80ddd --- /dev/null +++ b/trackers/core/bytetrack/utils.py @@ -0,0 +1,43 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +from collections.abc import Sequence + +from trackers.utils.base_tracklet import BaseTracklet + + +def _get_alive_tracklets( + tracklets: Sequence[BaseTracklet], + minimum_consecutive_frames: int, + maximum_frames_without_update: int, +) -> list[BaseTracklet]: + """ + Remove dead or immature lost tracklets and get alive trackers + that are within `maximum_frames_without_update` AND (it's mature OR + it was just updated). + + Args: + tracklets: List of BaseTracklet objects. + minimum_consecutive_frames: Number of consecutive frames that an object + must be tracked before it is considered a 'valid' track. + maximum_frames_without_update: Maximum number of frames without update + before a track is considered dead. + + Returns: + List of alive tracklets. + """ + alive_tracklets = [] + for tracklet in tracklets: + is_mature = ( + tracklet.number_of_successful_consecutive_updates + >= minimum_consecutive_frames + ) + is_active = tracklet.time_since_update == 0 + if tracklet.time_since_update < maximum_frames_without_update and ( + is_mature or is_active + ): + alive_tracklets.append(tracklet) + return alive_tracklets diff --git a/trackers/core/sort/utils.py b/trackers/core/sort/utils.py index f1fd93a5..23bcd020 100644 --- a/trackers/core/sort/utils.py +++ b/trackers/core/sort/utils.py @@ -9,21 +9,22 @@ import numpy as np import supervision as sv +from trackers.core.sort.tracklet import SORTTracklet from trackers.utils.base_tracklet import BaseTracklet def _get_alive_tracklets( - tracklets: Sequence[BaseTracklet], + tracklets: Sequence[SORTTracklet], minimum_consecutive_frames: int, maximum_frames_without_update: int, -) -> list[BaseTracklet]: +) -> list[SORTTracklet]: """ Remove dead or immature lost tracklets and get alive trackers that are within `maximum_frames_without_update` AND (it's mature OR it was just updated). Args: - tracklets: List of BaseTracklet objects. + tracklets: List of SORTTracklet objects. minimum_consecutive_frames: Number of consecutive frames that an object must be tracked before it is considered a 'valid' track. maximum_frames_without_update: Maximum number of frames without update @@ -34,10 +35,7 @@ def _get_alive_tracklets( """ alive_tracklets = [] for tracklet in tracklets: - is_mature = ( - tracklet.number_of_successful_updates - >= minimum_consecutive_frames - ) + is_mature = tracklet.number_of_successful_updates >= minimum_consecutive_frames is_active = tracklet.time_since_update == 0 if tracklet.time_since_update < maximum_frames_without_update and ( is_mature or is_active From d849ab82c80597628f19428ad721e814c032d193 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Thu, 12 Mar 2026 11:48:48 -0300 Subject: [PATCH 78/87] updated numbers --- docs/trackers/comparison.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/trackers/comparison.md b/docs/trackers/comparison.md index f877f68b..b0057efe 100644 --- a/docs/trackers/comparison.md +++ b/docs/trackers/comparison.md @@ -81,7 +81,7 @@ Sports broadcast tracking with fast motion, camera pans, and similar-looking tar | Tracker | HOTA | IDF1 | MOTA | | :-------: | :------: | :------: | :------: | - | SORT | 70.9 | 68.9 | 95.7 | + | SORT | 70.8 | 68.9 | 95.5 | | ByteTrack | **73.0** | **72.5** | **96.4** | | OC-SORT | 71.7 | 71.4 | 95.0 | @@ -152,7 +152,7 @@ Long sequences with dense interactions and partial occlusions. Tests long-term I | Tracker | HOTA | IDF1 | MOTA | | :-------: | :------: | :------: | :------: | | SORT | **84.2** | **78.2** | **98.2** | - | ByteTrack | 84.0 | 78.1 | 97.8 | + | ByteTrack | 84.0 | 78.1 | **98.2** | | OC-SORT | 82.9 | 77.9 | 96.8 | Tuned configuration for each tracker. @@ -166,9 +166,9 @@ Long sequences with dense interactions and partial occlusions. Tests long-term I ByteTrack: lost_track_buffer: 30 - track_activation_threshold: 0.5 - minimum_consecutive_frames: 2 - minimum_iou_threshold: 0.1 + track_activation_threshold: 0.2 + minimum_consecutive_frames: 1 + minimum_iou_threshold: 0.05 high_conf_det_threshold: 0.5 OC-SORT: From c22babbcc9512c0ddcc6664c8e7337a79c0e3d68 Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 13 Mar 2026 15:56:56 -0300 Subject: [PATCH 79/87] changed kalman filter name in tracklet to state_estimator --- trackers/core/bytetrack/tracklet.py | 14 ++++++------- trackers/core/ocsort/tracklet.py | 32 ++++++++++++++--------------- trackers/core/sort/tracklet.py | 16 +++++++-------- trackers/utils/base_tracklet.py | 2 +- 4 files changed, 32 insertions(+), 32 deletions(-) diff --git a/trackers/core/bytetrack/tracklet.py b/trackers/core/bytetrack/tracklet.py index 0755ccb3..dd63fa96 100644 --- a/trackers/core/bytetrack/tracklet.py +++ b/trackers/core/bytetrack/tracklet.py @@ -30,28 +30,28 @@ def __init__( def update(self, bbox: np.ndarray | None) -> None: """Update tracklet with new observation or None if missed.""" if bbox is not None: - self.kalman_filter.update(bbox) + self.state_estimator.update(bbox) self.time_since_update = 0 self.number_of_successful_consecutive_updates += 1 else: - self.kalman_filter.update(None) + self.state_estimator.update(None) self.time_since_update += 1 self.number_of_successful_consecutive_updates = 0 def predict(self) -> np.ndarray: """Predict next bounding box position.""" - self.kalman_filter.predict() + self.state_estimator.predict() self.age += 1 - return self.kalman_filter.state_to_bbox() + return self.state_estimator.state_to_bbox() def get_state_bbox(self) -> np.ndarray: """Get current bounding box estimate from the filter/state.""" - return self.kalman_filter.state_to_bbox() + return self.state_estimator.state_to_bbox() def _configure_noise(self) -> None: """Configure Kalman filter noise (original ByteTrack tuning).""" - kf = self.kalman_filter.kf - self.kalman_filter.set_kf_covariances( + kf = self.state_estimator.kf + self.state_estimator.set_kf_covariances( R=kf.R * 0.1, Q=kf.Q * 0.01, ) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index aac00d1b..387935b3 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -51,7 +51,7 @@ def __init__( Args: initial_bbox: Initial bounding box `[x1, y1, x2, y2]`. - kalman_filter_class: Kalman filter class to use. Instantiated + state_estimator_class: State estimator class to use. Instantiated with *initial_bbox*. Defaults to `XCYCSRKalmanFilter`. delta_t: Number of timesteps back to look for velocity estimation. @@ -75,7 +75,7 @@ def __init__( def _freeze(self) -> None: """Save Kalman filter state before track is lost (ORU mechanism).""" - self._frozen_state = self.kalman_filter.get_state() + self._frozen_state = self.state_estimator.get_state() def _unfreeze(self, new_bbox: np.ndarray) -> None: """Restore state and apply virtual trajectory (ORU mechanism). @@ -91,11 +91,11 @@ def _unfreeze(self, new_bbox: np.ndarray) -> None: return # Restore to frozen state - self.kalman_filter.set_state(self._frozen_state) + self.state_estimator.set_state(self._frozen_state) time_gap = self.time_since_update # this is oc-sort specific - if isinstance(self.kalman_filter, XCYCSRStateEstimator): + if isinstance(self.state_estimator, XCYCSRStateEstimator): self._unfreeze_xcycsr(new_bbox, time_gap) else: self._unfreeze_xyxy(new_bbox, time_gap) @@ -140,9 +140,9 @@ def _unfreeze_xcycsr(self, new_bbox: np.ndarray, time_gap: int) -> None: r = w / h virtual_obs = np.array([x, y, s, r]).reshape((4, 1)) - self.kalman_filter.kf.update(virtual_obs) + self.state_estimator.kf.update(virtual_obs) if i < time_gap - 1: - self.kalman_filter.kf.predict() + self.state_estimator.kf.predict() def _unfreeze_xyxy(self, new_bbox: np.ndarray, time_gap: int) -> None: """ORU interpolation for XYXY representation. @@ -159,9 +159,9 @@ def _unfreeze_xyxy(self, new_bbox: np.ndarray, time_gap: int) -> None: for i in range(time_gap): virtual_obs = (last_xyxy + (i + 1) * delta).reshape((4, 1)) - self.kalman_filter.kf.update(virtual_obs) + self.state_estimator.kf.update(virtual_obs) if i < time_gap - 1: - self.kalman_filter.kf.predict() + self.state_estimator.kf.predict() def get_k_previous_obs(self) -> np.ndarray | None: """Get observation from delta_t steps ago. @@ -224,7 +224,7 @@ def update(self, bbox: np.ndarray | None) -> None: # Update KF with the real observation # (after ORU this is the final update at the correct time step; # without ORU this is the normal measurement update) - self.kalman_filter.update(bbox) + self.state_estimator.update(bbox) self._observed = True self.time_since_update = 0 @@ -237,7 +237,7 @@ def update(self, bbox: np.ndarray | None) -> None: if self._observed: self._freeze() self._observed = False - self.kalman_filter.update(None) + self.state_estimator.update(None) def predict(self) -> np.ndarray: """Predict next bounding box position. @@ -245,14 +245,14 @@ def predict(self) -> np.ndarray: Returns: Predicted bounding box `[x1, y1, x2, y2]`. """ - self.kalman_filter.predict() + self.state_estimator.predict() self.age += 1 if self.time_since_update > 0: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 - return self.kalman_filter.state_to_bbox() + return self.state_estimator.state_to_bbox() def get_state_bbox(self) -> np.ndarray: """Get current bounding box estimate from Kalman filter. @@ -260,15 +260,15 @@ def get_state_bbox(self) -> np.ndarray: Returns: Current bounding box estimate `[x1, y1, x2, y2]`. """ - return self.kalman_filter.state_to_bbox() + return self.state_estimator.state_to_bbox() def _configure_noise(self) -> None: """Configure Kalman filter noise matrices (OC-SORT paper tuning).""" - kf = self.kalman_filter.kf + kf = self.state_estimator.kf R = kf.R P = kf.P Q = kf.Q - if isinstance(self.kalman_filter, XCYCSRStateEstimator): + if isinstance(self.state_estimator, XCYCSRStateEstimator): R[2:, 2:] *= 10.0 P[4:, 4:] *= 1000.0 P *= 10.0 @@ -279,7 +279,7 @@ def _configure_noise(self) -> None: P[4:, 4:] *= 1000.0 P *= 10.0 Q[4:, 4:] *= 0.01 - self.kalman_filter.set_kf_covariances(R=R, Q=Q, P=P) + self.state_estimator.set_kf_covariances(R=R, Q=Q, P=P) def resolve_tracker_id( self, diff --git a/trackers/core/sort/tracklet.py b/trackers/core/sort/tracklet.py index c9f50973..486a25da 100644 --- a/trackers/core/sort/tracklet.py +++ b/trackers/core/sort/tracklet.py @@ -32,31 +32,31 @@ def __init__( def update(self, bbox: np.ndarray | None) -> None: """Update tracklet with new observation or None if missed.""" if bbox is not None: - self.kalman_filter.update(bbox) + self.state_estimator.update(bbox) self.time_since_update = 0 self.number_of_successful_updates += 1 else: - self.kalman_filter.update(None) + self.state_estimator.update(None) self.time_since_update += 1 def predict(self) -> np.ndarray: """Predict next bounding box position.""" - self.kalman_filter.predict() + self.state_estimator.predict() self.age += 1 - return self.kalman_filter.state_to_bbox() + return self.state_estimator.state_to_bbox() def get_state_bbox(self) -> np.ndarray: """Get current bounding box estimate from the filter/state.""" - return self.kalman_filter.state_to_bbox() + return self.state_estimator.state_to_bbox() def _configure_noise(self) -> None: """Configure Kalman filter noise matrices (OC-SORT paper behaviour) and SORT behaviour for XYXY coordinates.""" - kf = self.kalman_filter.kf + kf = self.state_estimator.kf R = kf.R P = kf.P Q = kf.Q - if isinstance(self.kalman_filter, XCYCSRStateEstimator): + if isinstance(self.state_estimator, XCYCSRStateEstimator): R[2:, 2:] *= 10.0 P[4:, 4:] *= 1000.0 P *= 10.0 @@ -72,4 +72,4 @@ def _configure_noise(self) -> None: # Error covariance matrix (P) P = np.eye(8, dtype=np.float64) - self.kalman_filter.set_kf_covariances(R=R, Q=Q, P=P) + self.state_estimator.set_kf_covariances(R=R, Q=Q, P=P) diff --git a/trackers/utils/base_tracklet.py b/trackers/utils/base_tracklet.py index 9002d4da..77477885 100644 --- a/trackers/utils/base_tracklet.py +++ b/trackers/utils/base_tracklet.py @@ -23,7 +23,7 @@ def __init__( self, bbox: np.ndarray, state_estimator_class: type[BaseStateEstimator] ) -> None: self.age = 0 - self.kalman_filter: BaseStateEstimator = state_estimator_class(bbox) + self.state_estimator: BaseStateEstimator = state_estimator_class(bbox) self.tracker_id = -1 self.time_since_update = 0 From 668cf30f214647de0ab28485d79ca911b5b3eaee Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Fri, 13 Mar 2026 15:58:54 -0300 Subject: [PATCH 80/87] added first version of docs for custom kalmans --- docs/javascripts/mathjax.js | 12 ++ docs/learn/state-estimators.md | 243 +++++++++++++++++++++++++++++++++ mkdocs.yml | 6 + 3 files changed, 261 insertions(+) create mode 100644 docs/javascripts/mathjax.js create mode 100644 docs/learn/state-estimators.md diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js new file mode 100644 index 00000000..be1e8a19 --- /dev/null +++ b/docs/javascripts/mathjax.js @@ -0,0 +1,12 @@ +window.MathJax = { + tex: { + inlineMath: [["\\(", "\\)"], ["$", "$"]], + displayMath: [["\\[", "\\]"], ["$$", "$$"]], + processEscapes: true, + processEnvironments: true, + }, + options: { + ignoreHtmlClass: "^((?!arithmatex).)*$", + processHtmlClass: "arithmatex", + }, +}; diff --git a/docs/learn/state-estimators.md b/docs/learn/state-estimators.md new file mode 100644 index 00000000..336162e4 --- /dev/null +++ b/docs/learn/state-estimators.md @@ -0,0 +1,243 @@ +# State Estimators + +Every tracker in `trackers` uses a Kalman filter to predict where objects will appear in the next frame. The **state estimator** controls how bounding boxes are represented inside that filter. Different representations make different assumptions about object motion, and picking the right one can improve tracking quality without changing anything else. + +**What you'll learn:** + +- What state estimators are and why they matter +- How `XYXYStateEstimator` and `XCYCSRStateEstimator` represent bounding boxes +- When to use each representation +- How to swap the state estimator in any tracker + +--- + +## Install + +Get started by installing the package. + +```text +pip install trackers +``` + +For more options, see the [install guide](install.md). + +--- + +## What Is a State Estimator? + +A state estimator wraps a Kalman filter and defines how bounding boxes are encoded into the filter's state vector. The Kalman filter then predicts the next position of each tracked object and corrects that prediction when a new detection arrives. + +Two representations are available: + +| Estimator | State Dimensions | Representation | Aspect Ratio | +| :---------------------: | :--------------: | :---------------------------------------------------- | :-----------: | +| `XYXYStateEstimator` | 8 | Top-left and bottom-right corners + their velocities | Can change | +| `XCYCSRStateEstimator` | 7 | Center point, area, their velocities and aspect ratio | Held constant | + +They accept `[x1, y1, x2, y2]` bounding boxes on input and produce `[x1, y1, x2, y2]` bounding boxes on output. The difference is entirely in how the filter models motion internally. + +--- + +## XYXY — Corner-Based + +`XYXYStateEstimator` tracks the four corner coordinates independently. Each corner gets its own velocity term, giving the filter 8 state variables: + +``` +State: [x1, y1, x2, y2, vx1, vy1, vx2, vy2] +Measure: [x1, y1, x2, y2] +``` + +The transition matrix $F$ defines how the state evolves from one frame to the next. + +State order: $[x_1, y_1, x_2, y_2, v_{x_1}, v_{y_1}, v_{x_2}, v_{y_2}]$ + +$$ +F = +\begin{bmatrix} +1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ +0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 \\ +0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 \\ +0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\ +0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 +\end{bmatrix} +$$ + +Equivalent update equations: + +```text +x1' = x1 + vx1 +y1' = y1 + vy1 +x2' = x2 + vx2 +y2' = y2 + vy2 +vx1' = vx1 +vy1' = vy1 +vx2' = vx2 +vy2' = vy2 +``` + +| Row | Meaning | +| :-- | :------ | +| 1-4 | Each corner coordinate is updated by adding its velocity | +| 5-8 | Velocities persist unchanged from frame to frame | + +Because each corner moves freely, the box width and height can change between frames. This makes XYXY a natural fit when objects change shape — due to camera perspective, non-rigid motion, or inconsistent detections. + +**In Trackers, this is the default** for `ByteTrackTracker` and `SORTTracker`. + +--- + +## XCYCSR — Center-Based + +`XCYCSRStateEstimator` tracks the box center, area (scale), and aspect ratio. Only the center and scale get velocity terms; aspect ratio is treated as constant. This gives 7 state variables: + +``` +State: [x_center, y_center, scale, aspect_ratio, vx, vy, vs] +Measure: [x_center, y_center, scale, aspect_ratio] +``` + +The transition matrix $F$ shows the key difference: the aspect ratio is propagated without a velocity term. + +State order: $[x_c, y_c, s, r, v_x, v_y, v_s]$ + +$$ +F = +\begin{bmatrix} +1 & 0 & 0 & 0 & 1 & 0 & 0 \\ +0 & 1 & 0 & 0 & 0 & 1 & 0 \\ +0 & 0 & 1 & 0 & 0 & 0 & 1 \\ +0 & 0 & 0 & 1 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 1 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 1 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 1 +\end{bmatrix} +$$ + +Equivalent update equations: + +```text +x_center' = x_center + vx +y_center' = y_center + vy +scale' = scale + vs +aspect_ratio' = aspect_ratio +vx' = vx +vy' = vy +vs' = vs +``` + +| Row | Meaning | +| :-- | :------ | +| 1-3 | Center position and scale follow constant-velocity motion | +| 4 | Aspect ratio is copied forward unchanged | +| 5-7 | Velocities persist unchanged from frame to frame | + +The aspect ratio `r = w / h` is carried forward unchanged. This acts as a regularizer — the filter resists sudden shape changes. It works well for rigid objects whose proportions stay consistent, like pedestrians walking or cars on a highway. + +**This is the default** for `OCSORTTracker`, matching the original OC-SORT paper. + + +--- + +## When to Use Each + +| Scenario | Recommended | Why | +| :--------------------------------------------- | :-------------------: | :----------------------------------------------------------- | +| Pedestrians, vehicles, rigid objects | `XCYCSRStateEstimator`| Constant aspect ratio stabilizes predictions | +| Non-rigid or deformable objects | `XYXYStateEstimator` | Corners move independently to track shape changes | +| Noisy detections with fluctuating box sizes | `XCYCSRStateEstimator`| Aspect ratio constraint absorbs size noise | +| Strong perspective changes (camera pan/zoom) | `XYXYStateEstimator` | Box proportions shift with viewpoint; corners adapt freely | +| Default choice when unsure | `XYXYStateEstimator` | More general, fewer assumptions | + +--- + +## Swapping the Estimator + +All trackers accept a `state_estimator_class` parameter. Import the class you want and pass it to the constructor. + +=== "ByteTrack with XCYCSR" + + ```python + from trackers import ByteTrackTracker + from trackers.utils.state_representations import XCYCSRStateEstimator + + tracker = ByteTrackTracker( + state_estimator_class=XCYCSRStateEstimator, + ) + ``` + +=== "OC-SORT with XYXY" + + ```python + from trackers import OCSORTTracker + from trackers.utils.state_representations import XYXYStateEstimator + + tracker = OCSORTTracker( + state_estimator_class=XYXYStateEstimator, + ) + ``` + +=== "SORT with XCYCSR" + + ```python + from trackers import SORTTracker + from trackers.utils.state_representations import XCYCSRStateEstimator + + tracker = SORTTracker( + state_estimator_class=XCYCSRStateEstimator, + ) + ``` + +Everything else stays the same — detection, association, and visualization work identically regardless of which estimator you choose. + +--- + +## Full Example + +Run ByteTrack with both estimators on the same video and compare the results side by side. + +```python +import cv2 + +import supervision as sv +from inference import get_model +from trackers import ByteTrackTracker +from trackers.utils.state_representations import ( + XCYCSRStateEstimator, + XYXYStateEstimator, +) + +model = get_model("rfdetr-nano") + +tracker_xyxy = ByteTrackTracker( + state_estimator_class=XYXYStateEstimator, +) +tracker_xcycsr = ByteTrackTracker( + state_estimator_class=XCYCSRStateEstimator, +) + +cap = cv2.VideoCapture("source.mp4") +while True: + ret, frame = cap.read() + if not ret: + break + + result = model.infer(frame)[0] + detections = sv.Detections.from_inference(result) + + tracked_xyxy = tracker_xyxy.update(detections.copy()) + tracked_xcycsr = tracker_xcycsr.update(detections.copy()) + + # Compare tracker_id assignments, box smoothness, etc. + print(f"XYXY IDs: {tracked_xyxy.tracker_id}") + print(f"XCYCSR IDs: {tracked_xcycsr.tracker_id}") +``` + +--- + + + +## Takeaway + +The state estimator is a single-line change that controls how the Kalman filter models bounding box motion. Use `XCYCSRStateEstimator` when objects keep a consistent shape, and `XYXYStateEstimator` when shape varies or you want fewer assumptions. Try it on your case, the best choice depends on the scene. diff --git a/mkdocs.yml b/mkdocs.yml index dfa759b9..a3c97949 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -54,6 +54,8 @@ extra_css: extra_javascript: - javascripts/pycon_copy.js + - javascripts/mathjax.js + - https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js - javascripts/cli_builder_framework.js - javascripts/command_builder.js @@ -75,6 +77,9 @@ markdown_extensions: pygments_lang_class: true # Enables inline code highlighting - pymdownx.inlinehilite + # Enables LaTeX-style math in Markdown + - pymdownx.arithmatex: + generic: true # Allows including content from other files - pymdownx.snippets # Enables nested code blocks and custom fences @@ -120,6 +125,7 @@ nav: - Download Datasets: learn/download.md - Evaluate Trackers: learn/evaluate.md - Detection Quality Matters: learn/detection-quality.md + - State Estimators: learn/state-estimators.md - Trackers: - Comparison: trackers/comparison.md - SORT: trackers/sort.md From 15e95baa5a1a4e6e3c2bbf473e99bdc19f3a0b00 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 13 Mar 2026 18:59:14 +0000 Subject: [PATCH 81/87] =?UTF-8?q?fix(pre=5Fcommit):=20=F0=9F=8E=A8=20auto?= =?UTF-8?q?=20format=20pre-commit=20hooks?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/learn/state-estimators.md | 39 ++++++++++++++++------------------ 1 file changed, 18 insertions(+), 21 deletions(-) diff --git a/docs/learn/state-estimators.md b/docs/learn/state-estimators.md index 336162e4..cbfea242 100644 --- a/docs/learn/state-estimators.md +++ b/docs/learn/state-estimators.md @@ -29,10 +29,10 @@ A state estimator wraps a Kalman filter and defines how bounding boxes are encod Two representations are available: -| Estimator | State Dimensions | Representation | Aspect Ratio | -| :---------------------: | :--------------: | :---------------------------------------------------- | :-----------: | -| `XYXYStateEstimator` | 8 | Top-left and bottom-right corners + their velocities | Can change | -| `XCYCSRStateEstimator` | 7 | Center point, area, their velocities and aspect ratio | Held constant | +| Estimator | State Dimensions | Representation | Aspect Ratio | +| :--------------------: | :--------------: | :---------------------------------------------------- | :-----------: | +| `XYXYStateEstimator` | 8 | Top-left and bottom-right corners + their velocities | Can change | +| `XCYCSRStateEstimator` | 7 | Center point, area, their velocities and aspect ratio | Held constant | They accept `[x1, y1, x2, y2]` bounding boxes on input and produce `[x1, y1, x2, y2]` bounding boxes on output. The difference is entirely in how the filter models motion internally. @@ -78,10 +78,10 @@ vx2' = vx2 vy2' = vy2 ``` -| Row | Meaning | -| :-- | :------ | +| Row | Meaning | +| :-- | :------------------------------------------------------- | | 1-4 | Each corner coordinate is updated by adding its velocity | -| 5-8 | Velocities persist unchanged from frame to frame | +| 5-8 | Velocities persist unchanged from frame to frame | Because each corner moves freely, the box width and height can change between frames. This makes XYXY a natural fit when objects change shape — due to camera perspective, non-rigid motion, or inconsistent detections. @@ -127,28 +127,27 @@ vy' = vy vs' = vs ``` -| Row | Meaning | -| :-- | :------ | +| Row | Meaning | +| :-- | :-------------------------------------------------------- | | 1-3 | Center position and scale follow constant-velocity motion | -| 4 | Aspect ratio is copied forward unchanged | -| 5-7 | Velocities persist unchanged from frame to frame | +| 4 | Aspect ratio is copied forward unchanged | +| 5-7 | Velocities persist unchanged from frame to frame | The aspect ratio `r = w / h` is carried forward unchanged. This acts as a regularizer — the filter resists sudden shape changes. It works well for rigid objects whose proportions stay consistent, like pedestrians walking or cars on a highway. **This is the default** for `OCSORTTracker`, matching the original OC-SORT paper. - --- ## When to Use Each -| Scenario | Recommended | Why | -| :--------------------------------------------- | :-------------------: | :----------------------------------------------------------- | -| Pedestrians, vehicles, rigid objects | `XCYCSRStateEstimator`| Constant aspect ratio stabilizes predictions | -| Non-rigid or deformable objects | `XYXYStateEstimator` | Corners move independently to track shape changes | -| Noisy detections with fluctuating box sizes | `XCYCSRStateEstimator`| Aspect ratio constraint absorbs size noise | -| Strong perspective changes (camera pan/zoom) | `XYXYStateEstimator` | Box proportions shift with viewpoint; corners adapt freely | -| Default choice when unsure | `XYXYStateEstimator` | More general, fewer assumptions | +| Scenario | Recommended | Why | +| :------------------------------------------- | :--------------------: | :--------------------------------------------------------- | +| Pedestrians, vehicles, rigid objects | `XCYCSRStateEstimator` | Constant aspect ratio stabilizes predictions | +| Non-rigid or deformable objects | `XYXYStateEstimator` | Corners move independently to track shape changes | +| Noisy detections with fluctuating box sizes | `XCYCSRStateEstimator` | Aspect ratio constraint absorbs size noise | +| Strong perspective changes (camera pan/zoom) | `XYXYStateEstimator` | Box proportions shift with viewpoint; corners adapt freely | +| Default choice when unsure | `XYXYStateEstimator` | More general, fewer assumptions | --- @@ -236,8 +235,6 @@ while True: --- - - ## Takeaway The state estimator is a single-line change that controls how the Kalman filter models bounding box motion. Use `XCYCSRStateEstimator` when objects keep a consistent shape, and `XYXYStateEstimator` when shape varies or you want fewer assumptions. Try it on your case, the best choice depends on the scene. From c105d37bf5202622e51a2bc7218614f9773d100a Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Mon, 16 Mar 2026 11:02:09 -0300 Subject: [PATCH 82/87] removed copy from being passed to tracker --- docs/learn/state-estimators.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/learn/state-estimators.md b/docs/learn/state-estimators.md index 336162e4..8c749fed 100644 --- a/docs/learn/state-estimators.md +++ b/docs/learn/state-estimators.md @@ -226,8 +226,8 @@ while True: result = model.infer(frame)[0] detections = sv.Detections.from_inference(result) - tracked_xyxy = tracker_xyxy.update(detections.copy()) - tracked_xcycsr = tracker_xcycsr.update(detections.copy()) + tracked_xyxy = tracker_xyxy.update(detections) + tracked_xcycsr = tracker_xcycsr.update(detections) # Compare tracker_id assignments, box smoothness, etc. print(f"XYXY IDs: {tracked_xyxy.tracker_id}") From cdf24633485f4bc4927ce611aa6588d1d6b21cbe Mon Sep 17 00:00:00 2001 From: Alex Bodner Date: Tue, 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