ByteTrack

Initialize the ByteTrack object.

Parameters:

Name	Type	Description	Default
track_activation_threshold	float	Detection confidence threshold for track activation. Increasing track_activation_threshold improves accuracy and stability but might miss true detections. Decreasing it increases completeness but risks introducing noise and instability.	0.25
lost_track_buffer	int	Number of frames to buffer when a track is lost. Increasing lost_track_buffer enhances occlusion handling, significantly reducing the likelihood of track fragmentation or disappearance caused by brief detection gaps.	30
minimum_matching_threshold	float	Threshold for matching tracks with detections. Increasing minimum_matching_threshold improves accuracy but risks fragmentation. Decreasing it improves completeness but risks false positives and drift.	0.8
frame_rate	int	The frame rate of the video.	30
minimum_consecutive_frames	int	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.	1

Source code in supervision/tracker/byte_tracker/core.py

class ByteTrack:
    """
    Initialize the ByteTrack object.

    <video controls>
        <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-traces.mp4" type="video/mp4">
    </video>

    Parameters:
        track_activation_threshold (float, optional): Detection confidence threshold
            for track activation. Increasing track_activation_threshold improves accuracy
            and stability but might miss true detections. Decreasing it increases
            completeness but risks introducing noise and instability.
        lost_track_buffer (int, optional): Number of frames to buffer when a track is lost.
            Increasing lost_track_buffer enhances occlusion handling, significantly
            reducing the likelihood of track fragmentation or disappearance caused
            by brief detection gaps.
        minimum_matching_threshold (float, optional): Threshold for matching tracks with detections.
            Increasing minimum_matching_threshold improves accuracy but risks fragmentation.
            Decreasing it improves completeness but risks false positives and drift.
        frame_rate (int, optional): The frame rate of the video.
        minimum_consecutive_frames (int, optional): 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.
    """  # noqa: E501 // docs

    @deprecated_parameter(
        old_parameter="track_buffer",
        new_parameter="lost_track_buffer",
        map_function=lambda x: x,
        warning_message="`{old_parameter}` in `{function_name}` is deprecated and will "
        "be remove in `supervision-0.23.0`. Use '{new_parameter}' "
        "instead.",
    )
    @deprecated_parameter(
        old_parameter="track_thresh",
        new_parameter="track_activation_threshold",
        map_function=lambda x: x,
        warning_message="`{old_parameter}` in `{function_name}` is deprecated and will "
        "be remove in `supervision-0.23.0`. Use '{new_parameter}' "
        "instead.",
    )
    @deprecated_parameter(
        old_parameter="match_thresh",
        new_parameter="minimum_matching_threshold",
        map_function=lambda x: x,
        warning_message="`{old_parameter}` in `{function_name}` is deprecated and will "
        "be remove in `supervision-0.23.0`. Use '{new_parameter}' "
        "instead.",
    )
    def __init__(
        self,
        track_activation_threshold: float = 0.25,
        lost_track_buffer: int = 30,
        minimum_matching_threshold: float = 0.8,
        frame_rate: int = 30,
        minimum_consecutive_frames: int = 1,
    ):
        self.track_activation_threshold = track_activation_threshold
        self.minimum_matching_threshold = minimum_matching_threshold

        self.frame_id = 0
        self.det_thresh = self.track_activation_threshold + 0.1
        self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
        self.minimum_consecutive_frames = minimum_consecutive_frames
        self.kalman_filter = KalmanFilter()

        self.tracked_tracks: List[STrack] = []
        self.lost_tracks: List[STrack] = []
        self.removed_tracks: List[STrack] = []

    def update_with_detections(self, detections: Detections) -> Detections:
        """
        Updates the tracker with the provided detections and returns the updated
        detection results.

        Args:
            detections (Detections): The detections to pass through the tracker.

        Example:
            ```python
            import supervision as sv
            from ultralytics import YOLO

            model = YOLO(<MODEL_PATH>)
            tracker = sv.ByteTrack()

            bounding_box_annotator = sv.BoundingBoxAnnotator()
            label_annotator = sv.LabelAnnotator()

            def callback(frame: np.ndarray, index: int) -> np.ndarray:
                results = model(frame)[0]
                detections = sv.Detections.from_ultralytics(results)
                detections = tracker.update_with_detections(detections)

                labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

                annotated_frame = bounding_box_annotator.annotate(
                    scene=frame.copy(), detections=detections)
                annotated_frame = label_annotator.annotate(
                    scene=annotated_frame, detections=detections, labels=labels)
                return annotated_frame

            sv.process_video(
                source_path=<SOURCE_VIDEO_PATH>,
                target_path=<TARGET_VIDEO_PATH>,
                callback=callback
            )
            ```
        """

        tensors = detections2boxes(detections=detections)
        tracks = self.update_with_tensors(tensors=tensors)

        if len(tracks) > 0:
            detection_bounding_boxes = np.asarray([det[:4] for det in tensors])
            track_bounding_boxes = np.asarray([track.tlbr for track in tracks])

            ious = box_iou_batch(detection_bounding_boxes, track_bounding_boxes)

            iou_costs = 1 - ious

            matches, _, _ = matching.linear_assignment(iou_costs, 0.5)
            detections.tracker_id = np.full(len(detections), -1, dtype=int)
            for i_detection, i_track in matches:
                detections.tracker_id[i_detection] = int(
                    tracks[i_track].external_track_id
                )

            return detections[detections.tracker_id != -1]

        else:
            detections = Detections.empty()
            detections.tracker_id = np.array([], dtype=int)

            return detections

    def reset(self):
        """
        Resets the internal state of the ByteTrack tracker.

        This method clears the tracking data, including tracked, lost,
        and removed tracks, as well as resetting the frame counter. It's
        particularly useful when processing multiple videos sequentially,
        ensuring the tracker starts with a clean state for each new video.
        """
        self.frame_id = 0
        self.tracked_tracks: List[STrack] = []
        self.lost_tracks: List[STrack] = []
        self.removed_tracks: List[STrack] = []
        BaseTrack.reset_counter()
        STrack.reset_external_counter()

    def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
        """
        Updates the tracker with the provided tensors and returns the updated tracks.

        Parameters:
            tensors: The new tensors to update with.

        Returns:
            List[STrack]: Updated tracks.
        """
        self.frame_id += 1
        activated_starcks = []
        refind_stracks = []
        lost_stracks = []
        removed_stracks = []

        class_ids = tensors[:, 5]
        scores = tensors[:, 4]
        bboxes = tensors[:, :4]

        remain_inds = scores > self.track_activation_threshold
        inds_low = scores > 0.1
        inds_high = scores < self.track_activation_threshold

        inds_second = np.logical_and(inds_low, inds_high)
        dets_second = bboxes[inds_second]
        dets = bboxes[remain_inds]
        scores_keep = scores[remain_inds]
        scores_second = scores[inds_second]

        class_ids_keep = class_ids[remain_inds]
        class_ids_second = class_ids[inds_second]

        if len(dets) > 0:
            """Detections"""
            detections = [
                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
                for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
            ]
        else:
            detections = []

        """ Add newly detected tracklets to tracked_stracks"""
        unconfirmed = []
        tracked_stracks = []  # type: list[STrack]

        for track in self.tracked_tracks:
            if not track.is_activated:
                unconfirmed.append(track)
            else:
                tracked_stracks.append(track)

        """ Step 2: First association, with high score detection boxes"""
        strack_pool = joint_tracks(tracked_stracks, self.lost_tracks)
        # Predict the current location with KF
        STrack.multi_predict(strack_pool)
        dists = matching.iou_distance(strack_pool, detections)

        dists = matching.fuse_score(dists, detections)
        matches, u_track, u_detection = matching.linear_assignment(
            dists, thresh=self.minimum_matching_threshold
        )

        for itracked, idet in matches:
            track = strack_pool[itracked]
            det = detections[idet]
            if track.state == TrackState.Tracked:
                track.update(detections[idet], self.frame_id)
                activated_starcks.append(track)
            else:
                track.re_activate(det, self.frame_id, new_id=False)
                refind_stracks.append(track)

        """ Step 3: Second association, with low score detection boxes"""
        # association the untrack to the low score detections
        if len(dets_second) > 0:
            """Detections"""
            detections_second = [
                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
                for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
            ]
        else:
            detections_second = []
        r_tracked_stracks = [
            strack_pool[i]
            for i in u_track
            if strack_pool[i].state == TrackState.Tracked
        ]
        dists = matching.iou_distance(r_tracked_stracks, detections_second)
        matches, u_track, u_detection_second = matching.linear_assignment(
            dists, thresh=0.5
        )
        for itracked, idet in matches:
            track = r_tracked_stracks[itracked]
            det = detections_second[idet]
            if track.state == TrackState.Tracked:
                track.update(det, self.frame_id)
                activated_starcks.append(track)
            else:
                track.re_activate(det, self.frame_id, new_id=False)
                refind_stracks.append(track)

        for it in u_track:
            track = r_tracked_stracks[it]
            if not track.state == TrackState.Lost:
                track.mark_lost()
                lost_stracks.append(track)

        """Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
        detections = [detections[i] for i in u_detection]
        dists = matching.iou_distance(unconfirmed, detections)

        dists = matching.fuse_score(dists, detections)
        matches, u_unconfirmed, u_detection = matching.linear_assignment(
            dists, thresh=0.7
        )
        for itracked, idet in matches:
            unconfirmed[itracked].update(detections[idet], self.frame_id)
            activated_starcks.append(unconfirmed[itracked])
        for it in u_unconfirmed:
            track = unconfirmed[it]
            track.mark_removed()
            removed_stracks.append(track)

        """ Step 4: Init new stracks"""
        for inew in u_detection:
            track = detections[inew]
            if track.score < self.det_thresh:
                continue
            track.activate(self.kalman_filter, self.frame_id)
            activated_starcks.append(track)
        """ Step 5: Update state"""
        for track in self.lost_tracks:
            if self.frame_id - track.end_frame > self.max_time_lost:
                track.mark_removed()
                removed_stracks.append(track)

        self.tracked_tracks = [
            t for t in self.tracked_tracks if t.state == TrackState.Tracked
        ]
        self.tracked_tracks = joint_tracks(self.tracked_tracks, activated_starcks)
        self.tracked_tracks = joint_tracks(self.tracked_tracks, refind_stracks)
        self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks)
        self.lost_tracks.extend(lost_stracks)
        self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks)
        self.removed_tracks = removed_stracks
        self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
            self.tracked_tracks, self.lost_tracks
        )
        output_stracks = [track for track in self.tracked_tracks if track.is_activated]

        return output_stracks

Functions

reset()

Resets the internal state of the ByteTrack tracker.

This method clears the tracking data, including tracked, lost, and removed tracks, as well as resetting the frame counter. It's particularly useful when processing multiple videos sequentially, ensuring the tracker starts with a clean state for each new video.

Source code in supervision/tracker/byte_tracker/core.py

def reset(self):
    """
    Resets the internal state of the ByteTrack tracker.

    This method clears the tracking data, including tracked, lost,
    and removed tracks, as well as resetting the frame counter. It's
    particularly useful when processing multiple videos sequentially,
    ensuring the tracker starts with a clean state for each new video.
    """
    self.frame_id = 0
    self.tracked_tracks: List[STrack] = []
    self.lost_tracks: List[STrack] = []
    self.removed_tracks: List[STrack] = []
    BaseTrack.reset_counter()
    STrack.reset_external_counter()

update_with_detections(detections)

Updates the tracker with the provided detections and returns the updated detection results.

Parameters:

Name	Type	Description	Default
detections	Detections	The detections to pass through the tracker.	required

Example

import supervision as sv
from ultralytics import YOLO

model = YOLO(<MODEL_PATH>)
tracker = sv.ByteTrack()

bounding_box_annotator = sv.BoundingBoxAnnotator()
label_annotator = sv.LabelAnnotator()

def callback(frame: np.ndarray, index: int) -> np.ndarray:
    results = model(frame)[0]
    detections = sv.Detections.from_ultralytics(results)
    detections = tracker.update_with_detections(detections)

    labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

    annotated_frame = bounding_box_annotator.annotate(
        scene=frame.copy(), detections=detections)
    annotated_frame = label_annotator.annotate(
        scene=annotated_frame, detections=detections, labels=labels)
    return annotated_frame

sv.process_video(
    source_path=<SOURCE_VIDEO_PATH>,
    target_path=<TARGET_VIDEO_PATH>,
    callback=callback
)

Source code in supervision/tracker/byte_tracker/core.py

def update_with_detections(self, detections: Detections) -> Detections:
    """
    Updates the tracker with the provided detections and returns the updated
    detection results.

    Args:
        detections (Detections): The detections to pass through the tracker.

    Example:
        ```python
        import supervision as sv
        from ultralytics import YOLO

        model = YOLO(<MODEL_PATH>)
        tracker = sv.ByteTrack()

        bounding_box_annotator = sv.BoundingBoxAnnotator()
        label_annotator = sv.LabelAnnotator()

        def callback(frame: np.ndarray, index: int) -> np.ndarray:
            results = model(frame)[0]
            detections = sv.Detections.from_ultralytics(results)
            detections = tracker.update_with_detections(detections)

            labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

            annotated_frame = bounding_box_annotator.annotate(
                scene=frame.copy(), detections=detections)
            annotated_frame = label_annotator.annotate(
                scene=annotated_frame, detections=detections, labels=labels)
            return annotated_frame

        sv.process_video(
            source_path=<SOURCE_VIDEO_PATH>,
            target_path=<TARGET_VIDEO_PATH>,
            callback=callback
        )
        ```
    """

    tensors = detections2boxes(detections=detections)
    tracks = self.update_with_tensors(tensors=tensors)

    if len(tracks) > 0:
        detection_bounding_boxes = np.asarray([det[:4] for det in tensors])
        track_bounding_boxes = np.asarray([track.tlbr for track in tracks])

        ious = box_iou_batch(detection_bounding_boxes, track_bounding_boxes)

        iou_costs = 1 - ious

        matches, _, _ = matching.linear_assignment(iou_costs, 0.5)
        detections.tracker_id = np.full(len(detections), -1, dtype=int)
        for i_detection, i_track in matches:
            detections.tracker_id[i_detection] = int(
                tracks[i_track].external_track_id
            )

        return detections[detections.tracker_id != -1]

    else:
        detections = Detections.empty()
        detections.tracker_id = np.array([], dtype=int)

        return detections

update_with_tensors(tensors)

Updates the tracker with the provided tensors and returns the updated tracks.

Parameters:

Name	Type	Description	Default
tensors	ndarray	The new tensors to update with.	required

Returns:

Type	Description
List[STrack]	List[STrack]: Updated tracks.

Source code in supervision/tracker/byte_tracker/core.py

def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
    """
    Updates the tracker with the provided tensors and returns the updated tracks.

    Parameters:
        tensors: The new tensors to update with.

    Returns:
        List[STrack]: Updated tracks.
    """
    self.frame_id += 1
    activated_starcks = []
    refind_stracks = []
    lost_stracks = []
    removed_stracks = []

    class_ids = tensors[:, 5]
    scores = tensors[:, 4]
    bboxes = tensors[:, :4]

    remain_inds = scores > self.track_activation_threshold
    inds_low = scores > 0.1
    inds_high = scores < self.track_activation_threshold

    inds_second = np.logical_and(inds_low, inds_high)
    dets_second = bboxes[inds_second]
    dets = bboxes[remain_inds]
    scores_keep = scores[remain_inds]
    scores_second = scores[inds_second]

    class_ids_keep = class_ids[remain_inds]
    class_ids_second = class_ids[inds_second]

    if len(dets) > 0:
        """Detections"""
        detections = [
            STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
            for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
        ]
    else:
        detections = []

    """ Add newly detected tracklets to tracked_stracks"""
    unconfirmed = []
    tracked_stracks = []  # type: list[STrack]

    for track in self.tracked_tracks:
        if not track.is_activated:
            unconfirmed.append(track)
        else:
            tracked_stracks.append(track)

    """ Step 2: First association, with high score detection boxes"""
    strack_pool = joint_tracks(tracked_stracks, self.lost_tracks)
    # Predict the current location with KF
    STrack.multi_predict(strack_pool)
    dists = matching.iou_distance(strack_pool, detections)

    dists = matching.fuse_score(dists, detections)
    matches, u_track, u_detection = matching.linear_assignment(
        dists, thresh=self.minimum_matching_threshold
    )

    for itracked, idet in matches:
        track = strack_pool[itracked]
        det = detections[idet]
        if track.state == TrackState.Tracked:
            track.update(detections[idet], self.frame_id)
            activated_starcks.append(track)
        else:
            track.re_activate(det, self.frame_id, new_id=False)
            refind_stracks.append(track)

    """ Step 3: Second association, with low score detection boxes"""
    # association the untrack to the low score detections
    if len(dets_second) > 0:
        """Detections"""
        detections_second = [
            STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
            for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
        ]
    else:
        detections_second = []
    r_tracked_stracks = [
        strack_pool[i]
        for i in u_track
        if strack_pool[i].state == TrackState.Tracked
    ]
    dists = matching.iou_distance(r_tracked_stracks, detections_second)
    matches, u_track, u_detection_second = matching.linear_assignment(
        dists, thresh=0.5
    )
    for itracked, idet in matches:
        track = r_tracked_stracks[itracked]
        det = detections_second[idet]
        if track.state == TrackState.Tracked:
            track.update(det, self.frame_id)
            activated_starcks.append(track)
        else:
            track.re_activate(det, self.frame_id, new_id=False)
            refind_stracks.append(track)

    for it in u_track:
        track = r_tracked_stracks[it]
        if not track.state == TrackState.Lost:
            track.mark_lost()
            lost_stracks.append(track)

    """Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
    detections = [detections[i] for i in u_detection]
    dists = matching.iou_distance(unconfirmed, detections)

    dists = matching.fuse_score(dists, detections)
    matches, u_unconfirmed, u_detection = matching.linear_assignment(
        dists, thresh=0.7
    )
    for itracked, idet in matches:
        unconfirmed[itracked].update(detections[idet], self.frame_id)
        activated_starcks.append(unconfirmed[itracked])
    for it in u_unconfirmed:
        track = unconfirmed[it]
        track.mark_removed()
        removed_stracks.append(track)

    """ Step 4: Init new stracks"""
    for inew in u_detection:
        track = detections[inew]
        if track.score < self.det_thresh:
            continue
        track.activate(self.kalman_filter, self.frame_id)
        activated_starcks.append(track)
    """ Step 5: Update state"""
    for track in self.lost_tracks:
        if self.frame_id - track.end_frame > self.max_time_lost:
            track.mark_removed()
            removed_stracks.append(track)

    self.tracked_tracks = [
        t for t in self.tracked_tracks if t.state == TrackState.Tracked
    ]
    self.tracked_tracks = joint_tracks(self.tracked_tracks, activated_starcks)
    self.tracked_tracks = joint_tracks(self.tracked_tracks, refind_stracks)
    self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks)
    self.lost_tracks.extend(lost_stracks)
    self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks)
    self.removed_tracks = removed_stracks
    self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
        self.tracked_tracks, self.lost_tracks
    )
    output_stracks = [track for track in self.tracked_tracks if track.is_activated]

    return output_stracks

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