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ultralytics.trackers.byte_tracker.STrack

Bases : BaseTrack

Représentation du suivi d'un objet unique qui utilise le filtrage de Kalman pour l'estimation de l'état.

Cette classe est responsable du stockage de toutes les informations concernant les tracklets individuels et effectue des mises à jour d'état et des prédictions basées sur le filtre de Kalman.

Attributs :

Nom Type Description
shared_kalman KalmanFilterXYAH

Filtre de Kalman partagé qui est utilisé dans toutes les instances de STrack pour la prédiction.

_tlwh ndarray

Attribut privé permettant de stocker les coordonnées du coin supérieur gauche ainsi que la largeur et la hauteur de la boîte de délimitation.

kalman_filter KalmanFilterXYAH

Instance du filtre de Kalman utilisé pour cette piste d'objet particulière.

mean ndarray

Vecteur d'estimation de l'Ă©tat moyen.

covariance ndarray

Covariance de l'estimation de l'Ă©tat.

is_activated bool

Drapeau booléen indiquant si la piste a été activée.

score float

Score de confiance de la piste.

tracklet_len int

Longueur du tracklet.

cls any

Étiquette de classe pour l'objet.

idx int

Index ou identifiant de l'objet.

frame_id int

ID de la trame actuelle.

start_frame int

Cadre où l'objet a été détecté pour la première fois.

MĂ©thodes :

Nom Description
predict

Prédis le prochain état de l'objet à l'aide du filtre de Kalman.

multi_predict

Prédis les prochains états pour plusieurs pistes.

multi_gmc

Mets Ă  jour les Ă©tats de plusieurs pistes Ă  l'aide d'une matrice d'homographie.

activate

Active un nouveau tracklet.

re_activate

Réactive un tracklet précédemment perdu.

update

Met à jour l'état d'une piste jumelée.

convert_coords

Convertit la boîte de délimitation au format x-y-aspect-hauteur.

tlwh_to_xyah

Convertit la boîte de délimitation de tlwh au format xyah.

Code source dans 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

Obtenir les résultats actuels du suivi.

tlwh property

Obtenir la position actuelle dans le format de la boîte de délimitation (x en haut à gauche, y en haut à gauche, largeur, hauteur).

xywh property

Obtenir la position actuelle dans le format de la boîte de délimitation (centre x, centre y, largeur, hauteur).

xywha property

Obtenir la position actuelle dans le format de la boîte de délimitation (centre x, centre y, largeur, hauteur, angle).

xyxy property

Convertit la boîte de délimitation au format (min x, min y, max x, max y), c'est-à-dire (en haut à gauche, en bas à droite).

__init__(xywh, score, cls)

Initialise une nouvelle instance de STrack.

Code source dans 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__()

Renvoie une représentation sous forme de chaîne de l'objet BYTETracker avec les images de début et de fin et l'ID de la piste.

Code source dans 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)

Commence un nouveau tracklet.

Code source dans 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)

Convertit le format haut-gauche-largeur-hauteur d'une boîte de délimitation en son équivalent x-y-aspect-hauteur.

Code source dans 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

Mets Ă  jour les positions et les covariances des pistes d'Ă©tat Ă  l'aide d'une matrice d'homographie.

Code source dans 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

Effectue un suivi prédictif de plusieurs objets à l'aide d'un filtre de Kalman pour des pistes données.

Code source dans 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()

Prévoit la moyenne et la covariance à l'aide du filtre de Kalman.

Code source dans 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)

Réactive une piste précédemment perdue avec une nouvelle détection.

Code source dans 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

Convertit la boîte de délimitation au format (centre x, centre y, rapport d'aspect, hauteur), où le rapport d'aspect est largeur / hauteur.

Code source dans 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)

Met à jour l'état d'une piste jumelée.

Paramètres :

Nom Type Description DĂ©faut
new_track STrack

La nouvelle piste contenant des informations actualisées.

requis
frame_id int

L'ID de la trame actuelle.

requis
Code source dans 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 : Un algorithme de suivi construit sur YOLOv8 pour la détection et le suivi d'objets.

Cette classe est responsable de l'initialisation, de la mise à jour et de la gestion des pistes pour les objets détectés dans une séquence vidéo. séquence vidéo. Elle maintient l'état des pistes suivies, perdues et supprimées sur les images, utilise le filtrage de Kalman pour prédire l'emplacement des nouveaux objets et effectue l'association des données. prédire l'emplacement des nouveaux objets et effectue l'association des données.

Attributs :

Nom Type Description
tracked_stracks list[STrack]

Liste des pistes activées avec succès.

lost_stracks list[STrack]

Liste des pistes perdues.

removed_stracks list[STrack]

Liste des pistes supprimées.

frame_id int

L'ID de la trame actuelle.

args namespace

Arguments de la ligne de commande.

max_time_lost int

Le nombre maximum de trames pour qu'une piste soit considérée comme "perdue".

kalman_filter object

Objet du filtre de Kalman.

MĂ©thodes :

Nom Description
update

Met à jour le traqueur d'objets avec les nouvelles détections.

get_kalmanfilter

Renvoie un objet de filtre de Kalman pour le suivi des boîtes de délimitation.

init_track

Initialise le suivi des objets avec les détections.

get_dists

Calcule la distance entre les traces et les détections.

multi_predict

Prévoit l'emplacement des traces.

reset_id

RĂ©initialise le compteur d'ID de STrack.

joint_stracks

Combine deux listes d'éléments.

sub_stracks

Filtre les stracks présents dans la deuxième liste à partir de la première liste.

remove_duplicate_stracks

Supprime les stracks en double en fonction de l'unité d'exploitation.

Code source dans 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)

Initialise un objet YOLOv8 pour suivre les objets avec les arguments et la fréquence d'images donnés.

Code source dans 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)

Calcule la distance entre les pistes et les détections à l'aide des scores de l'IoU et des fusions.

Code source dans 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()

Renvoie un objet de filtre de Kalman pour le suivi des boîtes de délimitation.

Code source dans 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)

Initialise le suivi des objets avec les détections et les scores à l'aide de l'algorithme STrack.

Code source dans 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

Combine deux listes de stracks en une seule.

Code source dans 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)

Renvoie les pistes prédites à l'aide du réseau YOLOv8 .

Code source dans 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

Supprime les doublons dont la distance entre les points de contact n'est pas maximale.

Code source dans 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()

Remets le tracker à zéro.

Code source dans 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

RĂ©initialise le compteur d'ID de STrack.

Code source dans 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())

Code source dans 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)

Met à jour le traqueur d'objets avec les nouvelles détections et renvoie les boîtes de délimitation des objets suivis.

Code source dans 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)





Créé le 2023-11-12, Mis à jour le 2024-05-18
Auteurs : glenn-jocher (4), Burhan-Q (1), Laughing-q (1)