多目标跟踪——SORT算法

 Simple Online and Realtime Tracking (SORT) 是一种简洁、高效且实用的多目标跟踪算法。它以目标检测方法（如 YOLO）为基础，结合卡尔曼滤波与匈牙利算法，极大地提升了多目标跟踪的速度。然而，仅依靠 IOU 进行匹配虽能实现快速处理，却会导致严重的 ID switch 问题。SORT 的核心优势在于其算法架构，卡尔曼滤波负责预测目标位置，匈牙利算法则优化检测结果与跟踪目标的匹配，二者协同工作，确保了算法的高效运行。

import os
import numpy as np
import cv2
from filterpy.kalman import KalmanFilter
from ultralytics import YOLO

# ---------------------- SORT 算法及辅助函数 ---------------------- #
 
def linear_assignment(cost_matrix):
    try:
        import lap
        _, x, y = lap.lapjv(cost_matrix, extend_cost=True)
        return np.array([[y[i], i] for i in x if i >= 0])
    except ImportError:
        from scipy.optimize import linear_sum_assignment
        x, y = linear_sum_assignment(cost_matrix)
        return np.array(list(zip(x, y)))
 
def iou_batch(bb_test, bb_gt):
    bb_gt = np.expand_dims(bb_gt, 0)
    bb_test = np.expand_dims(bb_test, 1)
    xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
    yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
    xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
    yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
    w = np.maximum(0., xx2 - xx1)
    h = np.maximum(0., yy2 - yy1)
    wh = w * h
    o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])
              + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)
    return o
 
def convert_bbox_to_z(bbox):
    # bbox: [x1, y1, x2, y2]
    w = bbox[2] - bbox[0]
    h = bbox[3] - bbox[1]
    x = bbox[0] + w / 2.
    y = bbox[1] + h / 2.
    s = w * h
    r = w / float(h)
    return np.array([x, y, s, r]).reshape((4, 1))
 
def convert_x_to_bbox(x, score=None):
    # x: 状态向量 (7,1)
    w = np.sqrt(x[2] * x[3])
    h = x[2] / w
    if score is None:
        return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4))
    else:
        return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5))
 
class KalmanBoxTracker(object):
    count = 0
    def __init__(self, bbox):
        self.kf = KalmanFilter(dim_x=7, dim_z=4)
        # 状态：[center_x, center_y, s, r, vx, vy, vs]
        self.kf.F = np.array([[1, 0, 0, 0, 1, 0, 0],
                              [0, 1, 0, 0, 0, 1, 0],
                              [0, 0, 1, 0, 0, 0, 1],
                              [0, 0, 0, 1, 0, 0, 0],
                              [0, 0, 0, 0, 1, 0, 0],
                              [0, 0, 0, 0, 0, 1, 0],
                              [0, 0, 0, 0, 0, 0, 1]])
        self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0],
                              [0, 1, 0, 0, 0, 0, 0],
                              [0, 0, 1, 0, 0, 0, 0],
                              [0, 0, 0, 1, 0, 0, 0]])
        self.kf.R[2:, 2:] *= 10.
        self.kf.P[4:, 4:] *= 1000.
        self.kf.P *= 10.
        self.kf.Q[-1, -1] *= 0.01
        self.kf.Q[4:, 4:] *= 0.01
        self.kf.x[:4] = convert_bbox_to_z(bbox)
        self.time_since_update = 0
        self.id = KalmanBoxTracker.count
        KalmanBoxTracker.count += 1
        self.history = []
        self.hits = 0
        self.hit_streak = 0
        self.age = 0
 
    def update(self, bbox):
        self.time_since_update = 0
        self.history = []
        self.hits += 1
        self.hit_streak += 1
        self.kf.update(convert_bbox_to_z(bbox))
 
    def predict(self):
        # 若预测后的面积小于0则置零
        if ((self.kf.x[6] + self.kf.x[2]) <= 0):
            self.kf.x[6] *= 0.0
        self.kf.predict()
        self.age += 1
        if (self.time_since_update > 0):
            self.hit_streak = 0
        self.time_since_update += 1
        self.history.append(convert_x_to_bbox(self.kf.x))
        return self.history[-1]
 
    def get_state(self):
        return convert_x_to_bbox(self.kf.x)
 
def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3):
    if len(trackers) == 0:
        return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int)
    iou_matrix = iou_batch(detections, trackers)
    if min(iou_matrix.shape) > 0:
        a = (iou_matrix > iou_threshold).astype(np.int32)
        if a.sum(1).max() == 1 and a.sum(0).max() == 1:
            matched_indices = np.stack(np.where(a), axis=1)
        else:
            matched_indices = linear_assignment(-iou_matrix)
    else:
        matched_indices = np.empty(shape=(0, 2))
    unmatched_detections = []
    for d, det in enumerate(detections):
        if d not in matched_indices[:, 0]:
            unmatched_detections.append(d)
    unmatched_trackers = []
    for t, trk in enumerate(trackers):
        if t not in matched_indices[:, 1]:
            unmatched_trackers.append(t)
    matches = []
    for m in matched_indices:
        if iou_matrix[m[0], m[1]] < iou_threshold:
            unmatched_detections.append(m[0])
            unmatched_trackers.append(m[1])
        else:
            matches.append(m.reshape(1, 2))
    if len(matches) == 0:
        matches = np.empty((0, 2), dtype=int)
    else:
        matches = np.concatenate(matches, axis=0)
    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
 
class Sort(object):
    def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
        self.max_age = max_age
        self.min_hits = min_hits
        self.iou_threshold = iou_threshold
        self.trackers = []
        self.frame_count = 0
 
    def update(self, dets=np.empty((0, 5))):
        self.frame_count += 1
        trks = np.zeros((len(self.trackers), 5))
        to_del = []
        ret = []
        for t, trk in enumerate(self.trackers):
            pos = trk.predict()[0]
            trks[t] = [pos[0], pos[1], pos[2], pos[3], 0]
            if np.any(np.isnan(pos)):
                to_del.append(t)
        if len(to_del) > 0:
            for t in reversed(to_del):
                self.trackers.pop(t)
        matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.iou_threshold)
        for m in matched:
            self.trackers[m[1]].update(dets[m[0], :])
        for i in unmatched_dets:
            trk = KalmanBoxTracker(dets[i, :])
            self.trackers.append(trk)
        i = len(self.trackers)
        for trk in reversed(self.trackers):
            d = trk.get_state()[0]
            if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
                ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))
            i -= 1
            if trk.time_since_update > self.max_age:
                self.trackers.pop(i)
        if len(ret) > 0:
            return np.concatenate(ret)
        return np.empty((0, 5))
 
# ---------------------- YOLOv8 检测模块 ---------------------- #
def yolo_detect(frame, model, conf_threshold=0.5):
    """
    使用 YOLOv8 检测图像中的目标（例如 person）。
    返回格式为：[[x1, y1, x2, y2, score], ...]
    """
    results = model(frame)
    # 检查检测结果
    if results and results[0].boxes is not None and len(results[0].boxes) > 0:
        # 获取检测框（xyxy 格式）和置信度
        boxes = results[0].boxes.xyxy.cpu().numpy() if hasattr(results[0].boxes.xyxy, 'cpu') else results[0].boxes.xyxy.numpy()
        confs = results[0].boxes.conf.cpu().numpy() if hasattr(results[0].boxes.conf, 'cpu') else results[0].boxes.conf.numpy()
        # 若需要筛选特定类别，可以参考下面的代码（此处默认所有检测均有效）
        # cls = results[0].boxes.cls.cpu().numpy() if hasattr(results[0].boxes.cls, 'cpu') else results[0].boxes.cls.numpy()
        # mask = (cls == 0)  # 假设只保留类别为 person（COCO 中 class_id == 0）
        # boxes = boxes[mask]
        # confs = confs[mask]
        dets = np.hstack((boxes, confs.reshape(-1, 1)))
    else:
        dets = np.empty((0, 5))
    return dets # x1 y1 x2 y2 score
 
# ---------------------- 主函数 ---------------------- #
def main():
    # 加载 YOLOv8 模型
    yolo_weights = r"yolov8n.pt"  # YOLO 权重文件路径
    model = YOLO(yolo_weights)
 
    # 初始化 SORT 跟踪器
    max_age, min_hits, iou_threshold = 3, 3, 0.3
    mot_tracker = Sort(max_age=max_age, min_hits=min_hits, iou_threshold=iou_threshold)
    colours = 255 * np.random.rand(32, 3)

    # 打开视频流，这里使用摄像头；若使用视频文件，将参数改为文件路径
    cap = cv2.VideoCapture(0)
    if not cap.isOpened():
        print("无法打开视频流")
        return
 
    while True:
        ret, frame = cap.read()
        if not ret:
            break
 
        # 使用 YOLOv8 检测
        dets = yolo_detect(frame, model)
 
        # 更新 SORT 跟踪器
        tracked_objects = mot_tracker.update(dets)
        
        # 绘制跟踪结果
        for d in tracked_objects:
            # d: [x1, y1, x2, y2, track_id]
            x1, y1, x2, y2, track_id = d.astype(int)
            color = colours[int(track_id) % 32].tolist()
            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
            cv2.putText(frame, str(int(track_id)), (x1, y1 - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
        cv2.imshow("YOLOv8 + SORT Tracking", frame)
        key = cv2.waitKey(1)
        if key == 27:  # 按 Esc 键退出
            break
 
    cap.release()
    cv2.destroyAllWindows()
 
if __name__ == '__main__':
    main()