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Reference for ultralytics/trackers/byte_tracker.py

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ultralytics.trackers.byte_tracker.STrack

STrack(xywh, score, cls)

Bases: BaseTrack

Single object tracking representation that uses Kalman filtering for state estimation.

This class is responsible for storing all the information regarding individual tracklets and performs state updates and predictions based on Kalman filter.

Attributes:

Name Type Description
shared_kalman KalmanFilterXYAH

Shared Kalman filter that is used across all STrack instances for prediction.

_tlwh ndarray

Private attribute to store top-left corner coordinates and width and height of bounding box.

kalman_filter KalmanFilterXYAH

Instance of Kalman filter used for this particular object track.

mean ndarray

Mean state estimate vector.

covariance ndarray

Covariance of state estimate.

is_activated bool

Boolean flag indicating if the track has been activated.

score float

Confidence score of the track.

tracklet_len int

Length of the tracklet.

cls any

Class label for the object.

idx int

Index or identifier for the object.

frame_id int

Current frame ID.

start_frame int

Frame where the object was first detected.

Methods:

Name Description
predict

Predict the next state of the object using Kalman filter.

multi_predict

Predict the next states for multiple tracks.

multi_gmc

Update multiple track states using a homography matrix.

activate

Activate a new tracklet.

re_activate

Reactivate a previously lost tracklet.

update

Update the state of a matched track.

convert_coords

Convert bounding box to x-y-aspect-height format.

tlwh_to_xyah

Convert tlwh bounding box to xyah format.

Source code in ultralytics/trackers/byte_tracker.py
def __init__(self, xywh, score, cls):
    """Initialize new STrack instance."""
    super().__init__()
    # xywh+idx or xywha+idx
    assert len(xywh) in {5, 6}, f"expected 5 or 6 values but got {len(xywh)}"
    self._tlwh = np.asarray(xywh2ltwh(xywh[:4]), dtype=np.float32)
    self.kalman_filter = None
    self.mean, self.covariance = None, None
    self.is_activated = False

    self.score = score
    self.tracklet_len = 0
    self.cls = cls
    self.idx = xywh[-1]
    self.angle = xywh[4] if len(xywh) == 6 else None

result property

result

Get current tracking results.

tlwh property

tlwh

Get current position in bounding box format (top left x, top left y, width, height).

xywh property

xywh

Get current position in bounding box format (center x, center y, width, height).

xywha property

xywha

Get current position in bounding box format (center x, center y, width, height, angle).

xyxy property

xyxy

Convert bounding box to format (min x, min y, max x, max y), i.e., (top left, bottom right).

__repr__

__repr__()

Return a string representation of the BYTETracker object with start and end frames and track ID.

Source code in ultralytics/trackers/byte_tracker.py
def __repr__(self):
    """Return a string representation of the BYTETracker object with start and end frames and track ID."""
    return f"OT_{self.track_id}_({self.start_frame}-{self.end_frame})"

activate

activate(kalman_filter, frame_id)

Start a new tracklet.

Source code in ultralytics/trackers/byte_tracker.py
def activate(self, kalman_filter, frame_id):
    """Start a new tracklet."""
    self.kalman_filter = kalman_filter
    self.track_id = self.next_id()
    self.mean, self.covariance = self.kalman_filter.initiate(self.convert_coords(self._tlwh))

    self.tracklet_len = 0
    self.state = TrackState.Tracked
    if frame_id == 1:
        self.is_activated = True
    self.frame_id = frame_id
    self.start_frame = frame_id

convert_coords

convert_coords(tlwh)

Convert a bounding box's top-left-width-height format to its x-y-aspect-height equivalent.

Source code in ultralytics/trackers/byte_tracker.py
def convert_coords(self, tlwh):
    """Convert a bounding box's top-left-width-height format to its x-y-aspect-height equivalent."""
    return self.tlwh_to_xyah(tlwh)

multi_gmc staticmethod

multi_gmc(stracks, H=np.eye(2, 3))

Update state tracks positions and covariances using a homography matrix.

Source code in ultralytics/trackers/byte_tracker.py
@staticmethod
def multi_gmc(stracks, H=np.eye(2, 3)):
    """Update state tracks positions and covariances using a homography matrix."""
    if len(stracks) > 0:
        multi_mean = np.asarray([st.mean.copy() for st in stracks])
        multi_covariance = np.asarray([st.covariance for st in stracks])

        R = H[:2, :2]
        R8x8 = np.kron(np.eye(4, dtype=float), R)
        t = H[:2, 2]

        for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
            mean = R8x8.dot(mean)
            mean[:2] += t
            cov = R8x8.dot(cov).dot(R8x8.transpose())

            stracks[i].mean = mean
            stracks[i].covariance = cov

multi_predict staticmethod

multi_predict(stracks)

Perform multi-object predictive tracking using Kalman filter for given stracks.

Source code in ultralytics/trackers/byte_tracker.py
@staticmethod
def multi_predict(stracks):
    """Perform multi-object predictive tracking using Kalman filter for given stracks."""
    if len(stracks) <= 0:
        return
    multi_mean = np.asarray([st.mean.copy() for st in stracks])
    multi_covariance = np.asarray([st.covariance for st in stracks])
    for i, st in enumerate(stracks):
        if st.state != TrackState.Tracked:
            multi_mean[i][7] = 0
    multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
    for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
        stracks[i].mean = mean
        stracks[i].covariance = cov

predict

predict()

Predicts mean and covariance using Kalman filter.

Source code in ultralytics/trackers/byte_tracker.py
def predict(self):
    """Predicts mean and covariance using Kalman filter."""
    mean_state = self.mean.copy()
    if self.state != TrackState.Tracked:
        mean_state[7] = 0
    self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)

re_activate

re_activate(new_track, frame_id, new_id=False)

Reactivates a previously lost track with a new detection.

Source code in ultralytics/trackers/byte_tracker.py
def re_activate(self, new_track, frame_id, new_id=False):
    """Reactivates a previously lost track with a new detection."""
    self.mean, self.covariance = self.kalman_filter.update(
        self.mean, self.covariance, self.convert_coords(new_track.tlwh)
    )
    self.tracklet_len = 0
    self.state = TrackState.Tracked
    self.is_activated = True
    self.frame_id = frame_id
    if new_id:
        self.track_id = self.next_id()
    self.score = new_track.score
    self.cls = new_track.cls
    self.angle = new_track.angle
    self.idx = new_track.idx

tlwh_to_xyah staticmethod

tlwh_to_xyah(tlwh)

Convert bounding box to format (center x, center y, aspect ratio, height), where the aspect ratio is width / height.

Source code in ultralytics/trackers/byte_tracker.py
@staticmethod
def tlwh_to_xyah(tlwh):
    """Convert bounding box to format (center x, center y, aspect ratio, height), where the aspect ratio is width /
    height.
    """
    ret = np.asarray(tlwh).copy()
    ret[:2] += ret[2:] / 2
    ret[2] /= ret[3]
    return ret

update

update(new_track, frame_id)

Update the state of a matched track.

Parameters:

Name Type Description Default
new_track STrack

The new track containing updated information.

required
frame_id int

The ID of the current frame.

required
Source code in ultralytics/trackers/byte_tracker.py
def update(self, new_track, frame_id):
    """
    Update the state of a matched track.

    Args:
        new_track (STrack): The new track containing updated information.
        frame_id (int): The ID of the current frame.
    """
    self.frame_id = frame_id
    self.tracklet_len += 1

    new_tlwh = new_track.tlwh
    self.mean, self.covariance = self.kalman_filter.update(
        self.mean, self.covariance, self.convert_coords(new_tlwh)
    )
    self.state = TrackState.Tracked
    self.is_activated = True

    self.score = new_track.score
    self.cls = new_track.cls
    self.angle = new_track.angle
    self.idx = new_track.idx





ultralytics.trackers.byte_tracker.BYTETracker

BYTETracker(args, frame_rate=30)

BYTETracker: A tracking algorithm built on top of YOLOv8 for object detection and tracking.

The class is responsible for initializing, updating, and managing the tracks for detected objects in a video sequence. It maintains the state of tracked, lost, and removed tracks over frames, utilizes Kalman filtering for predicting the new object locations, and performs data association.

Attributes:

Name Type Description
tracked_stracks list[STrack]

List of successfully activated tracks.

lost_stracks list[STrack]

List of lost tracks.

removed_stracks list[STrack]

List of removed tracks.

frame_id int

The current frame ID.

args namespace

Command-line arguments.

max_time_lost int

The maximum frames for a track to be considered as 'lost'.

kalman_filter object

Kalman Filter object.

Methods:

Name Description
update

Updates object tracker with new detections.

get_kalmanfilter

Returns a Kalman filter object for tracking bounding boxes.

init_track

Initialize object tracking with detections.

get_dists

Calculates the distance between tracks and detections.

multi_predict

Predicts the location of tracks.

reset_id

Resets the ID counter of STrack.

joint_stracks

Combines two lists of stracks.

sub_stracks

Filters out the stracks present in the second list from the first list.

remove_duplicate_stracks

Removes duplicate stracks based on IoU.

Source code in ultralytics/trackers/byte_tracker.py
def __init__(self, args, frame_rate=30):
    """Initialize a YOLOv8 object to track objects with given arguments and frame rate."""
    self.tracked_stracks = []  # type: list[STrack]
    self.lost_stracks = []  # type: list[STrack]
    self.removed_stracks = []  # type: list[STrack]

    self.frame_id = 0
    self.args = args
    self.max_time_lost = int(frame_rate / 30.0 * args.track_buffer)
    self.kalman_filter = self.get_kalmanfilter()
    self.reset_id()

get_dists

get_dists(tracks, detections)

Calculates the distance between tracks and detections using IoU and fuses scores.

Source code in ultralytics/trackers/byte_tracker.py
def get_dists(self, tracks, detections):
    """Calculates the distance between tracks and detections using IoU and fuses scores."""
    dists = matching.iou_distance(tracks, detections)
    # TODO: mot20
    # if not self.args.mot20:
    dists = matching.fuse_score(dists, detections)
    return dists

get_kalmanfilter

get_kalmanfilter()

Returns a Kalman filter object for tracking bounding boxes.

Source code in ultralytics/trackers/byte_tracker.py
def get_kalmanfilter(self):
    """Returns a Kalman filter object for tracking bounding boxes."""
    return KalmanFilterXYAH()

init_track

init_track(dets, scores, cls, img=None)

Initialize object tracking with detections and scores using STrack algorithm.

Source code in ultralytics/trackers/byte_tracker.py
def init_track(self, dets, scores, cls, img=None):
    """Initialize object tracking with detections and scores using STrack algorithm."""
    return [STrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)] if len(dets) else []  # detections

joint_stracks staticmethod

joint_stracks(tlista, tlistb)

Combine two lists of stracks into a single one.

Source code in ultralytics/trackers/byte_tracker.py
@staticmethod
def joint_stracks(tlista, tlistb):
    """Combine two lists of stracks into a single one."""
    exists = {}
    res = []
    for t in tlista:
        exists[t.track_id] = 1
        res.append(t)
    for t in tlistb:
        tid = t.track_id
        if not exists.get(tid, 0):
            exists[tid] = 1
            res.append(t)
    return res

multi_predict

multi_predict(tracks)

Returns the predicted tracks using the YOLOv8 network.

Source code in ultralytics/trackers/byte_tracker.py
def multi_predict(self, tracks):
    """Returns the predicted tracks using the YOLOv8 network."""
    STrack.multi_predict(tracks)

remove_duplicate_stracks staticmethod

remove_duplicate_stracks(stracksa, stracksb)

Remove duplicate stracks with non-maximum IoU distance.

Source code in ultralytics/trackers/byte_tracker.py
@staticmethod
def remove_duplicate_stracks(stracksa, stracksb):
    """Remove duplicate stracks with non-maximum IoU distance."""
    pdist = matching.iou_distance(stracksa, stracksb)
    pairs = np.where(pdist < 0.15)
    dupa, dupb = [], []
    for p, q in zip(*pairs):
        timep = stracksa[p].frame_id - stracksa[p].start_frame
        timeq = stracksb[q].frame_id - stracksb[q].start_frame
        if timep > timeq:
            dupb.append(q)
        else:
            dupa.append(p)
    resa = [t for i, t in enumerate(stracksa) if i not in dupa]
    resb = [t for i, t in enumerate(stracksb) if i not in dupb]
    return resa, resb

reset

reset()

Reset tracker.

Source code in ultralytics/trackers/byte_tracker.py
def reset(self):
    """Reset tracker."""
    self.tracked_stracks = []  # type: list[STrack]
    self.lost_stracks = []  # type: list[STrack]
    self.removed_stracks = []  # type: list[STrack]
    self.frame_id = 0
    self.kalman_filter = self.get_kalmanfilter()
    self.reset_id()

reset_id staticmethod

reset_id()

Resets the ID counter of STrack.

Source code in ultralytics/trackers/byte_tracker.py
@staticmethod
def reset_id():
    """Resets the ID counter of STrack."""
    STrack.reset_id()

sub_stracks staticmethod

sub_stracks(tlista, tlistb)

DEPRECATED CODE in https://github.com/ultralytics/ultralytics/pull/1890/ stracks = {t.track_id: t for t in tlista} for t in tlistb: tid = t.track_id if stracks.get(tid, 0): del stracks[tid] return list(stracks.values())

Source code in ultralytics/trackers/byte_tracker.py
@staticmethod
def sub_stracks(tlista, tlistb):
    """DEPRECATED CODE in https://github.com/ultralytics/ultralytics/pull/1890/
    stracks = {t.track_id: t for t in tlista}
    for t in tlistb:
        tid = t.track_id
        if stracks.get(tid, 0):
            del stracks[tid]
    return list(stracks.values())
    """
    track_ids_b = {t.track_id for t in tlistb}
    return [t for t in tlista if t.track_id not in track_ids_b]

update

update(results, img=None)

Updates object tracker with new detections and returns tracked object bounding boxes.

Source code in ultralytics/trackers/byte_tracker.py
def update(self, results, img=None):
    """Updates object tracker with new detections and returns tracked object bounding boxes."""
    self.frame_id += 1
    activated_stracks = []
    refind_stracks = []
    lost_stracks = []
    removed_stracks = []

    scores = results.conf
    bboxes = results.xywhr if hasattr(results, "xywhr") else results.xywh
    # Add index
    bboxes = np.concatenate([bboxes, np.arange(len(bboxes)).reshape(-1, 1)], axis=-1)
    cls = results.cls

    remain_inds = scores >= self.args.track_high_thresh
    inds_low = scores > self.args.track_low_thresh
    inds_high = scores < self.args.track_high_thresh

    inds_second = inds_low & inds_high
    dets_second = bboxes[inds_second]
    dets = bboxes[remain_inds]
    scores_keep = scores[remain_inds]
    scores_second = scores[inds_second]
    cls_keep = cls[remain_inds]
    cls_second = cls[inds_second]

    detections = self.init_track(dets, scores_keep, cls_keep, img)
    # Add newly detected tracklets to tracked_stracks
    unconfirmed = []
    tracked_stracks = []  # type: list[STrack]
    for track in self.tracked_stracks:
        if not track.is_activated:
            unconfirmed.append(track)
        else:
            tracked_stracks.append(track)
    # Step 2: First association, with high score detection boxes
    strack_pool = self.joint_stracks(tracked_stracks, self.lost_stracks)
    # Predict the current location with KF
    self.multi_predict(strack_pool)
    if hasattr(self, "gmc") and img is not None:
        warp = self.gmc.apply(img, dets)
        STrack.multi_gmc(strack_pool, warp)
        STrack.multi_gmc(unconfirmed, warp)

    dists = self.get_dists(strack_pool, detections)
    matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh)

    for itracked, idet in matches:
        track = strack_pool[itracked]
        det = detections[idet]
        if track.state == TrackState.Tracked:
            track.update(det, self.frame_id)
            activated_stracks.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
    detections_second = self.init_track(dets_second, scores_second, cls_second, img)
    r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
    # TODO
    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_stracks.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 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 = self.get_dists(unconfirmed, 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_stracks.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.args.new_track_thresh:
            continue
        track.activate(self.kalman_filter, self.frame_id)
        activated_stracks.append(track)
    # Step 5: Update state
    for track in self.lost_stracks:
        if self.frame_id - track.end_frame > self.max_time_lost:
            track.mark_removed()
            removed_stracks.append(track)

    self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
    self.tracked_stracks = self.joint_stracks(self.tracked_stracks, activated_stracks)
    self.tracked_stracks = self.joint_stracks(self.tracked_stracks, refind_stracks)
    self.lost_stracks = self.sub_stracks(self.lost_stracks, self.tracked_stracks)
    self.lost_stracks.extend(lost_stracks)
    self.lost_stracks = self.sub_stracks(self.lost_stracks, self.removed_stracks)
    self.tracked_stracks, self.lost_stracks = self.remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
    self.removed_stracks.extend(removed_stracks)
    if len(self.removed_stracks) > 1000:
        self.removed_stracks = self.removed_stracks[-999:]  # clip remove stracks to 1000 maximum

    return np.asarray([x.result for x in self.tracked_stracks if x.is_activated], dtype=np.float32)





Created 2023-11-12, Updated 2024-07-21
Authors: glenn-jocher (6), Burhan-Q (1), Laughing-q (1)