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参考资料 ultralytics/trackers/bot_sort.py

备注

该文件可在https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/trackers/bot_sort .py 上获取。如果您发现问题,请通过提交 Pull Request🛠️ 帮助修复。谢谢🙏!



ultralytics.trackers.bot_sort.BOTrack

垒球 STrack

STrack 类的扩展版本,用于YOLOv8 ,增加了对象跟踪功能。

属性

名称 类型 说明
shared_kalman KalmanFilterXYWH

用于 BOTrack 所有实例的共享卡尔曼滤波器。

smooth_feat ndarray

平滑特征向量

curr_feat ndarray

当前特征向量。

features deque

存储特征向量的 deque,其最大长度定义为 feat_history.

alpha float

特征指数移动平均值的平滑系数。

mean ndarray

卡尔曼滤波器的平均状态。

covariance ndarray

卡尔曼滤波器的协方差矩阵。

方法

名称 说明
update_features

更新特征向量,并使用指数移动平均法对其进行平滑处理。

predict

使用卡尔曼滤波器预测均值和协方差。

re_activate

以更新的功能和可选的新 ID 重新激活轨道。

update

使用新的轨道和帧 ID 更新YOLOv8 实例。

tlwh

以 tlwh 格式获取当前位置的属性 (top left x, top left y, width, height).

multi_predict

使用共享卡尔曼滤波器预测多个物体轨迹的平均值和协方差。

convert_coords

将 tlwh 边界框坐标转换为 xywh 格式。

tlwh_to_xywh

将边界框转换为 xywh 格式 (center x, center y, width, height).

使用方法

bo_track = BOTrack(tlwh, score, cls, feat) bo_track.predict() bo_track.update(new_track, frame_id)

源代码 ultralytics/trackers/bot_sort.py
class BOTrack(STrack):
    """
    An extended version of the STrack class for YOLOv8, adding object tracking features.

    Attributes:
        shared_kalman (KalmanFilterXYWH): A shared Kalman filter for all instances of BOTrack.
        smooth_feat (np.ndarray): Smoothed feature vector.
        curr_feat (np.ndarray): Current feature vector.
        features (deque): A deque to store feature vectors with a maximum length defined by `feat_history`.
        alpha (float): Smoothing factor for the exponential moving average of features.
        mean (np.ndarray): The mean state of the Kalman filter.
        covariance (np.ndarray): The covariance matrix of the Kalman filter.

    Methods:
        update_features(feat): Update features vector and smooth it using exponential moving average.
        predict(): Predicts the mean and covariance using Kalman filter.
        re_activate(new_track, frame_id, new_id): Reactivates a track with updated features and optionally new ID.
        update(new_track, frame_id): Update the YOLOv8 instance with new track and frame ID.
        tlwh: Property that gets the current position in tlwh format `(top left x, top left y, width, height)`.
        multi_predict(stracks): Predicts the mean and covariance of multiple object tracks using shared Kalman filter.
        convert_coords(tlwh): Converts tlwh bounding box coordinates to xywh format.
        tlwh_to_xywh(tlwh): Convert bounding box to xywh format `(center x, center y, width, height)`.

    Usage:
        bo_track = BOTrack(tlwh, score, cls, feat)
        bo_track.predict()
        bo_track.update(new_track, frame_id)
    """

    shared_kalman = KalmanFilterXYWH()

    def __init__(self, tlwh, score, cls, feat=None, feat_history=50):
        """Initialize YOLOv8 object with temporal parameters, such as feature history, alpha and current features."""
        super().__init__(tlwh, score, cls)

        self.smooth_feat = None
        self.curr_feat = None
        if feat is not None:
            self.update_features(feat)
        self.features = deque([], maxlen=feat_history)
        self.alpha = 0.9

    def update_features(self, feat):
        """Update features vector and smooth it using exponential moving average."""
        feat /= np.linalg.norm(feat)
        self.curr_feat = feat
        if self.smooth_feat is None:
            self.smooth_feat = feat
        else:
            self.smooth_feat = self.alpha * self.smooth_feat + (1 - self.alpha) * feat
        self.features.append(feat)
        self.smooth_feat /= np.linalg.norm(self.smooth_feat)

    def predict(self):
        """Predicts the mean and covariance using Kalman filter."""
        mean_state = self.mean.copy()
        if self.state != TrackState.Tracked:
            mean_state[6] = 0
            mean_state[7] = 0

        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)

    def re_activate(self, new_track, frame_id, new_id=False):
        """Reactivates a track with updated features and optionally assigns a new ID."""
        if new_track.curr_feat is not None:
            self.update_features(new_track.curr_feat)
        super().re_activate(new_track, frame_id, new_id)

    def update(self, new_track, frame_id):
        """Update the YOLOv8 instance with new track and frame ID."""
        if new_track.curr_feat is not None:
            self.update_features(new_track.curr_feat)
        super().update(new_track, frame_id)

    @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[2:] / 2
        return ret

    @staticmethod
    def multi_predict(stracks):
        """Predicts the mean and covariance of multiple object tracks using shared Kalman filter."""
        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][6] = 0
                multi_mean[i][7] = 0
        multi_mean, multi_covariance = BOTrack.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

    def convert_coords(self, tlwh):
        """Converts Top-Left-Width-Height bounding box coordinates to X-Y-Width-Height format."""
        return self.tlwh_to_xywh(tlwh)

    @staticmethod
    def tlwh_to_xywh(tlwh):
        """Convert bounding box to format `(center x, center y, width, height)`."""
        ret = np.asarray(tlwh).copy()
        ret[:2] += ret[2:] / 2
        return ret

tlwh property

以边界框格式获取当前位置 (top left x, top left y, width, height).

__init__(tlwh, score, cls, feat=None, feat_history=50)

使用时间参数(如特征历史、alpha 和当前特征)初始化YOLOv8 对象。

源代码 ultralytics/trackers/bot_sort.py
def __init__(self, tlwh, score, cls, feat=None, feat_history=50):
    """Initialize YOLOv8 object with temporal parameters, such as feature history, alpha and current features."""
    super().__init__(tlwh, score, cls)

    self.smooth_feat = None
    self.curr_feat = None
    if feat is not None:
        self.update_features(feat)
    self.features = deque([], maxlen=feat_history)
    self.alpha = 0.9

convert_coords(tlwh)

将顶部-左侧-宽度-高度边界框坐标转换为 X-Y 宽度-高度格式。

源代码 ultralytics/trackers/bot_sort.py
def convert_coords(self, tlwh):
    """Converts Top-Left-Width-Height bounding box coordinates to X-Y-Width-Height format."""
    return self.tlwh_to_xywh(tlwh)

multi_predict(stracks) staticmethod

使用共享卡尔曼滤波器预测多个物体轨迹的平均值和协方差。

源代码 ultralytics/trackers/bot_sort.py
@staticmethod
def multi_predict(stracks):
    """Predicts the mean and covariance of multiple object tracks using shared Kalman filter."""
    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][6] = 0
            multi_mean[i][7] = 0
    multi_mean, multi_covariance = BOTrack.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()

使用卡尔曼滤波器预测均值和协方差。

源代码 ultralytics/trackers/bot_sort.py
def predict(self):
    """Predicts the mean and covariance using Kalman filter."""
    mean_state = self.mean.copy()
    if self.state != TrackState.Tracked:
        mean_state[6] = 0
        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)

用更新的功能重新激活轨道,并可选择分配一个新的 ID。

源代码 ultralytics/trackers/bot_sort.py
def re_activate(self, new_track, frame_id, new_id=False):
    """Reactivates a track with updated features and optionally assigns a new ID."""
    if new_track.curr_feat is not None:
        self.update_features(new_track.curr_feat)
    super().re_activate(new_track, frame_id, new_id)

tlwh_to_xywh(tlwh) staticmethod

将边界框转换为格式 (center x, center y, width, height).

源代码 ultralytics/trackers/bot_sort.py
@staticmethod
def tlwh_to_xywh(tlwh):
    """Convert bounding box to format `(center x, center y, width, height)`."""
    ret = np.asarray(tlwh).copy()
    ret[:2] += ret[2:] / 2
    return ret

update(new_track, frame_id)

使用新的轨道和帧 ID 更新YOLOv8 实例。

源代码 ultralytics/trackers/bot_sort.py
def update(self, new_track, frame_id):
    """Update the YOLOv8 instance with new track and frame ID."""
    if new_track.curr_feat is not None:
        self.update_features(new_track.curr_feat)
    super().update(new_track, frame_id)

update_features(feat)

更新特征向量,并使用指数移动平均法对其进行平滑处理。

源代码 ultralytics/trackers/bot_sort.py
def update_features(self, feat):
    """Update features vector and smooth it using exponential moving average."""
    feat /= np.linalg.norm(feat)
    self.curr_feat = feat
    if self.smooth_feat is None:
        self.smooth_feat = feat
    else:
        self.smooth_feat = self.alpha * self.smooth_feat + (1 - self.alpha) * feat
    self.features.append(feat)
    self.smooth_feat /= np.linalg.norm(self.smooth_feat)



ultralytics.trackers.bot_sort.BOTSORT

垒球 BYTETracker

BYTETracker 类的扩展版本,用于YOLOv8 ,专为使用 ReID 和 GMC 算法进行物体跟踪而设计。

属性

名称 类型 说明
proximity_thresh float

轨迹与检测之间的空间接近度(IoU)阈值。

appearance_thresh float

轨迹与检测之间的外观相似度(ReID 嵌入)阈值。

encoder object

处理 ReID 嵌入的对象,如果未启用 ReID,则设置为 "无"。

gmc GMC

用于数据关联的 GMC 算法实例。

args object

解析的命令行参数,包含跟踪参数。

方法

名称 说明
get_kalmanfilter

返回用于对象跟踪的 KalmanFilterXYWH 的实例。

init_track

用检测结果、分数和类别初始化轨道。

get_dists

使用 IoU 和(可选)ReID 获取轨道和探测点之间的距离。

multi_predict

利用YOLOv8 模型预测和跟踪多个物体。

使用方法

bot_sort = BOTSORT(args, frame_rate) bot_sort.init_track(dets, scores, cls, img) bot_sort.multi_predict(tracks)

备注

该类旨在与YOLOv8 对象检测模型配合使用,仅在通过 args 启用时才支持 ReID。

源代码 ultralytics/trackers/bot_sort.py
class BOTSORT(BYTETracker):
    """
    An extended version of the BYTETracker class for YOLOv8, designed for object tracking with ReID and GMC algorithm.

    Attributes:
        proximity_thresh (float): Threshold for spatial proximity (IoU) between tracks and detections.
        appearance_thresh (float): Threshold for appearance similarity (ReID embeddings) between tracks and detections.
        encoder (object): Object to handle ReID embeddings, set to None if ReID is not enabled.
        gmc (GMC): An instance of the GMC algorithm for data association.
        args (object): Parsed command-line arguments containing tracking parameters.

    Methods:
        get_kalmanfilter(): Returns an instance of KalmanFilterXYWH for object tracking.
        init_track(dets, scores, cls, img): Initialize track with detections, scores, and classes.
        get_dists(tracks, detections): Get distances between tracks and detections using IoU and (optionally) ReID.
        multi_predict(tracks): Predict and track multiple objects with YOLOv8 model.

    Usage:
        bot_sort = BOTSORT(args, frame_rate)
        bot_sort.init_track(dets, scores, cls, img)
        bot_sort.multi_predict(tracks)

    Note:
        The class is designed to work with the YOLOv8 object detection model and supports ReID only if enabled via args.
    """

    def __init__(self, args, frame_rate=30):
        """Initialize YOLOv8 object with ReID module and GMC algorithm."""
        super().__init__(args, frame_rate)
        # ReID module
        self.proximity_thresh = args.proximity_thresh
        self.appearance_thresh = args.appearance_thresh

        if args.with_reid:
            # Haven't supported BoT-SORT(reid) yet
            self.encoder = None
        self.gmc = GMC(method=args.gmc_method)

    def get_kalmanfilter(self):
        """Returns an instance of KalmanFilterXYWH for object tracking."""
        return KalmanFilterXYWH()

    def init_track(self, dets, scores, cls, img=None):
        """Initialize track with detections, scores, and classes."""
        if len(dets) == 0:
            return []
        if self.args.with_reid and self.encoder is not None:
            features_keep = self.encoder.inference(img, dets)
            return [BOTrack(xyxy, s, c, f) for (xyxy, s, c, f) in zip(dets, scores, cls, features_keep)]  # detections
        else:
            return [BOTrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)]  # detections

    def get_dists(self, tracks, detections):
        """Get distances between tracks and detections using IoU and (optionally) ReID embeddings."""
        dists = matching.iou_distance(tracks, detections)
        dists_mask = dists > self.proximity_thresh

        # TODO: mot20
        # if not self.args.mot20:
        dists = matching.fuse_score(dists, detections)

        if self.args.with_reid and self.encoder is not None:
            emb_dists = matching.embedding_distance(tracks, detections) / 2.0
            emb_dists[emb_dists > self.appearance_thresh] = 1.0
            emb_dists[dists_mask] = 1.0
            dists = np.minimum(dists, emb_dists)
        return dists

    def multi_predict(self, tracks):
        """Predict and track multiple objects with YOLOv8 model."""
        BOTrack.multi_predict(tracks)

    def reset(self):
        """Reset tracker."""
        super().reset()
        self.gmc.reset_params()

__init__(args, frame_rate=30)

使用 ReID 模块和 GMC 算法初始化YOLOv8 对象。

源代码 ultralytics/trackers/bot_sort.py
def __init__(self, args, frame_rate=30):
    """Initialize YOLOv8 object with ReID module and GMC algorithm."""
    super().__init__(args, frame_rate)
    # ReID module
    self.proximity_thresh = args.proximity_thresh
    self.appearance_thresh = args.appearance_thresh

    if args.with_reid:
        # Haven't supported BoT-SORT(reid) yet
        self.encoder = None
    self.gmc = GMC(method=args.gmc_method)

get_dists(tracks, detections)

使用 IoU 和(可选)ReID 嵌入,获取轨迹和检测之间的距离。

源代码 ultralytics/trackers/bot_sort.py
def get_dists(self, tracks, detections):
    """Get distances between tracks and detections using IoU and (optionally) ReID embeddings."""
    dists = matching.iou_distance(tracks, detections)
    dists_mask = dists > self.proximity_thresh

    # TODO: mot20
    # if not self.args.mot20:
    dists = matching.fuse_score(dists, detections)

    if self.args.with_reid and self.encoder is not None:
        emb_dists = matching.embedding_distance(tracks, detections) / 2.0
        emb_dists[emb_dists > self.appearance_thresh] = 1.0
        emb_dists[dists_mask] = 1.0
        dists = np.minimum(dists, emb_dists)
    return dists

get_kalmanfilter()

返回用于对象跟踪的 KalmanFilterXYWH 的实例。

源代码 ultralytics/trackers/bot_sort.py
def get_kalmanfilter(self):
    """Returns an instance of KalmanFilterXYWH for object tracking."""
    return KalmanFilterXYWH()

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

用检测结果、分数和类别初始化轨道。

源代码 ultralytics/trackers/bot_sort.py
def init_track(self, dets, scores, cls, img=None):
    """Initialize track with detections, scores, and classes."""
    if len(dets) == 0:
        return []
    if self.args.with_reid and self.encoder is not None:
        features_keep = self.encoder.inference(img, dets)
        return [BOTrack(xyxy, s, c, f) for (xyxy, s, c, f) in zip(dets, scores, cls, features_keep)]  # detections
    else:
        return [BOTrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)]  # detections

multi_predict(tracks)

利用YOLOv8 模型预测和跟踪多个物体。

源代码 ultralytics/trackers/bot_sort.py
def multi_predict(self, tracks):
    """Predict and track multiple objects with YOLOv8 model."""
    BOTrack.multi_predict(tracks)

reset()

重置跟踪器。

源代码 ultralytics/trackers/bot_sort.py
def reset(self):
    """Reset tracker."""
    super().reset()
    self.gmc.reset_params()





创建于 2023-11-12,更新于 2023-11-25
作者:glenn-jocher(3),Laughing-q(1)