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Referenz fĂŒr ultralytics/trackers/byte_tracker.py

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ultralytics.trackers.byte_tracker.STrack

Basen: BaseTrack

Darstellung der Einzelobjektverfolgung, die Kalman-Filterung fĂŒr die ZustandsschĂ€tzung verwendet.

Diese Klasse ist fĂŒr die Speicherung aller Informationen zu den einzelnen Tracklets verantwortlich und fĂŒhrt Zustandsaktualisierungen und Vorhersagen basierend auf dem Kalman-Filter.

Attribute:

Name Typ Beschreibung
shared_kalman KalmanFilterXYAH

Gemeinsamer Kalman-Filter, der fĂŒr alle STrack-Instanzen zur Vorhersage verwendet wird.

_tlwh ndarray

Privates Attribut zum Speichern der Koordinaten der linken oberen Ecke sowie der Breite und Höhe des Begrenzungsrahmens.

kalman_filter KalmanFilterXYAH

Instanz des Kalman-Filters, der fĂŒr diese spezielle Objektspur verwendet wird.

mean ndarray

Mittlerer Vektor der ZustandsschÀtzung.

covariance ndarray

Kovarianz der ZustandsschÀtzung.

is_activated bool

Boolesches Flag, das angibt, ob der Track aktiviert wurde.

score float

Vertrauenspunkte der Strecke.

tracklet_len int

LĂ€nge des Tracklets.

cls any

Klassenbezeichnung fĂŒr das Objekt.

idx int

Index oder Bezeichner fĂŒr das Objekt.

frame_id int

Aktuelle Frame-ID.

start_frame int

Rahmen, in dem das Objekt zuerst entdeckt wurde.

Methoden:

Name Beschreibung
predict

Sage den nÀchsten Zustand des Objekts mit dem Kalman-Filter voraus.

multi_predict

Sage die nĂ€chsten ZustĂ€nde fĂŒr mehrere Spuren voraus.

multi_gmc

Aktualisiere mehrere GleiszustÀnde mithilfe einer Homografie-Matrix.

activate

Aktiviere ein neues Tracklet.

re_activate

Reaktiviere ein zuvor verlorenes Tracklet.

update

Aktualisiere den Status eines zugeordneten Tracks.

convert_coords

Konvertiere die Bounding Box in das Format x-y-aspect-height.

tlwh_to_xyah

Konvertiere tlwh Bounding Box in das xyah-Format.

Quellcode in ultralytics/trackers/byte_tracker.py
class STrack(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:
        shared_kalman (KalmanFilterXYAH): Shared Kalman filter that is used across all STrack instances for prediction.
        _tlwh (np.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 (np.ndarray): Mean state estimate vector.
        covariance (np.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:
        predict(): Predict the next state of the object using Kalman filter.
        multi_predict(stracks): Predict the next states for multiple tracks.
        multi_gmc(stracks, H): Update multiple track states using a homography matrix.
        activate(kalman_filter, frame_id): Activate a new tracklet.
        re_activate(new_track, frame_id, new_id): Reactivate a previously lost tracklet.
        update(new_track, frame_id): Update the state of a matched track.
        convert_coords(tlwh): Convert bounding box to x-y-aspect-height format.
        tlwh_to_xyah(tlwh): Convert tlwh bounding box to xyah format.
    """

    shared_kalman = KalmanFilterXYAH()

    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

    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)

    @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

    @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

    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

    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

    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

    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)

    @property
    def tlwh(self):
        """Get current position in bounding box format (top left x, top left y, width, height)."""
        if self.mean is None:
            return self._tlwh.copy()
        ret = self.mean[:4].copy()
        ret[2] *= ret[3]
        ret[:2] -= ret[2:] / 2
        return ret

    @property
    def xyxy(self):
        """Convert bounding box to format (min x, min y, max x, max y), i.e., (top left, bottom right)."""
        ret = self.tlwh.copy()
        ret[2:] += ret[:2]
        return ret

    @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

    @property
    def xywh(self):
        """Get current position in bounding box format (center x, center y, width, height)."""
        ret = np.asarray(self.tlwh).copy()
        ret[:2] += ret[2:] / 2
        return ret

    @property
    def xywha(self):
        """Get current position in bounding box format (center x, center y, width, height, angle)."""
        if self.angle is None:
            LOGGER.warning("WARNING ⚠ `angle` attr not found, returning `xywh` instead.")
            return self.xywh
        return np.concatenate([self.xywh, self.angle[None]])

    @property
    def result(self):
        """Get current tracking results."""
        coords = self.xyxy if self.angle is None else self.xywha
        return coords.tolist() + [self.track_id, self.score, self.cls, self.idx]

    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})"

result property

Erhalte aktuelle Tracking-Ergebnisse.

tlwh property

Ermittelt die aktuelle Position im Bounding-Box-Format (oben links x, oben links y, Breite, Höhe).

xywh property

Ermittelt die aktuelle Position im Bounding-Box-Format (Mitte x, Mitte y, Breite, Höhe).

xywha property

Ermittelt die aktuelle Position im Bounding-Box-Format (Mitte x, Mitte y, Breite, Höhe, Winkel).

xyxy property

Konvertiere den Begrenzungsrahmen in das Format (min x, min y, max x, max y), d.h. (oben links, unten rechts).

__init__(xywh, score, cls)

Initialisiere eine neue STrack-Instanz.

Quellcode 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

__repr__()

Gibt eine String-ReprĂ€sentation des BYTETracker-Objekts mit Start- und Endframes und Track-ID zurĂŒck.

Quellcode 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(kalman_filter, frame_id)

Beginne ein neues Tracklet.

Quellcode 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(tlwh)

Konvertiert das Format "Oben links - Breite - Höhe" eines Begrenzungsrahmens in sein Äquivalent "X-Y-Aspekt - Höhe".

Quellcode 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(stracks, H=np.eye(2, 3)) staticmethod

Aktualisiere die Positionen und Kovarianzen der State Tracks mithilfe einer Homografie-Matrix.

Quellcode 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(stracks) staticmethod

FĂŒhre eine prĂ€diktive Verfolgung mehrerer Objekte mithilfe des Kalman-Filters fĂŒr gegebene Strecken durch.

Quellcode 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()

Sagt Mittelwert und Kovarianz mithilfe des Kalman-Filters voraus.

Quellcode 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(new_track, frame_id, new_id=False)

Reaktiviert eine zuvor verlorene Spur mit einer neuen Erkennung.

Quellcode 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(tlwh) staticmethod

Bounding Box in das Format (Mitte x, Mitte y, SeitenverhÀltnis, Höhe) konvertieren, wobei das SeitenverhÀltnis Breite / Höhe ist. Höhe ist.

Quellcode 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(new_track, frame_id)

Aktualisiere den Status eines zugeordneten Tracks.

Parameter:

Name Typ Beschreibung Standard
new_track STrack

Der neue Track mit aktualisierten Informationen.

erforderlich
frame_id int

Die ID des aktuellen Rahmens.

erforderlich
Quellcode 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: Ein Tracking-Algorithmus, der auf YOLOv8 aufbaut, um Objekte zu erkennen und zu verfolgen.

Die Klasse ist fĂŒr das Initialisieren, Aktualisieren und Verwalten der Spuren fĂŒr erkannte Objekte in einer Videosequenz verantwortlich. Sequenz. Sie verwaltet den Status der verfolgten, verlorenen und entfernten Spuren ĂŒber die Frames hinweg, nutzt die Kalman-Filterung zur Vorhersage der neuen Objektpositionen und fĂŒhrt eine Datenzuordnung durch.

Attribute:

Name Typ Beschreibung
tracked_stracks list[STrack]

Liste der erfolgreich aktivierten Tracks.

lost_stracks list[STrack]

Liste der verlorenen Spuren.

removed_stracks list[STrack]

Liste der entfernten Titel.

frame_id int

Die aktuelle Frame-ID.

args namespace

Befehlszeilenargumente.

max_time_lost int

Die maximalen Frames fĂŒr einen Track, der als "verloren" gilt.

kalman_filter object

Kalman Filter Objekt.

Methoden:

Name Beschreibung
update

Aktualisiert den Objekt-Tracker mit neuen Entdeckungen.

get_kalmanfilter

Gibt ein Kalman-Filter-Objekt zum Verfolgen von Bounding Boxes zurĂŒck.

init_track

Initialisiere die Objektverfolgung mit Erkennungen.

get_dists

Berechnet den Abstand zwischen Spuren und Erkennungen.

multi_predict

Sagt die Position von Spuren voraus.

reset_id

Setzt den ID-ZĂ€hler von STrack zurĂŒck.

joint_stracks

Kombiniert zwei Listen von Stracks.

sub_stracks

Filtert die in der zweiten Liste vorhandenen StÀmme aus der ersten Liste heraus.

remove_duplicate_stracks

Entfernt doppelte StÀmme basierend auf IoU.

Quellcode in ultralytics/trackers/byte_tracker.py
class BYTETracker:
    """
    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:
        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:
        update(results, img=None): Updates object tracker with new detections.
        get_kalmanfilter(): Returns a Kalman filter object for tracking bounding boxes.
        init_track(dets, scores, cls, img=None): Initialize object tracking with detections.
        get_dists(tracks, detections): Calculates the distance between tracks and detections.
        multi_predict(tracks): Predicts the location of tracks.
        reset_id(): Resets the ID counter of STrack.
        joint_stracks(tlista, tlistb): Combines two lists of stracks.
        sub_stracks(tlista, tlistb): Filters out the stracks present in the second list from the first list.
        remove_duplicate_stracks(stracksa, stracksb): Removes duplicate stracks based on IoU.
    """

    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()

    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)

    def get_kalmanfilter(self):
        """Returns a Kalman filter object for tracking bounding boxes."""
        return KalmanFilterXYAH()

    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

    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

    def multi_predict(self, tracks):
        """Returns the predicted tracks using the YOLOv8 network."""
        STrack.multi_predict(tracks)

    @staticmethod
    def reset_id():
        """Resets the ID counter of STrack."""
        STrack.reset_id()

    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()

    @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

    @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]

    @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

__init__(args, frame_rate=30)

Initialisiere ein YOLOv8 Objekt, um Objekte mit den angegebenen Argumenten und der Bildrate zu verfolgen.

Quellcode 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(tracks, detections)

Berechnet den Abstand zwischen Spuren und Erkennungen mithilfe von IoU und verschmilzt die Punkte.

Quellcode 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()

Gibt ein Kalman-Filter-Objekt zum Verfolgen von Bounding Boxes zurĂŒck.

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

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

Initialisiere die Objektverfolgung mit Erkennungen und Punktzahlen mit dem STrack-Algorithmus.

Quellcode 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(tlista, tlistb) staticmethod

Verbinde zwei Listen mit StÀmmen zu einer einzigen.

Quellcode 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(tracks)

Gibt die vorhergesagten Spuren mit dem YOLOv8 Netzwerk zurĂŒck.

Quellcode 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(stracksa, stracksb) staticmethod

Entferne doppelte StÀmme mit nicht maximalem IoU-Abstand.

Quellcode 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()

Tracker zurĂŒcksetzen.

Quellcode 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

Setzt den ID-ZĂ€hler von STrack zurĂŒck.

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

sub_stracks(tlista, tlistb) staticmethod

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())

Quellcode 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(results, img=None)

Aktualisiert den Objekt-Tracker mit neuen Erkennungen und gibt die Boundingboxen der verfolgten Objekte zurĂŒck.

Quellcode 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 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1), Laughing-q (1)