【目标跟踪】多目标跟踪sort （python 代码）

前言

1. 多目标跟踪 sort（Simple Online Realtime Tracking）是一种基于目标检测的跟踪。
2. 根据我自己的理解把它分为个5步骤。
   1. 初始化航迹。当第一帧检测结果输入时，此时航迹（trackers）为空，此时航迹保存检测结果。等待下一帧检测结果输入。
   2. 预测。如果航迹不为空，航迹会预测一个 predict_box。
   3. 匹配。预测的一个predict_box 与此时进来的检测结果 detect_box 匈牙利匹配（代码计算的iou）。
   4. 更新。匹配成功的目标，用 detect_box 去修正 predict_box。未匹配成功的目标，重新起航迹或者舍弃。
   5. 输出结果，等待下一帧检测目标进来重复步骤 2，3，4。
3. 对于算法细节这里不进行赘述。感兴趣可以参考博主往期博客。【目标跟踪】匈牙利算法 【目标跟踪】卡尔曼滤波（公式推导与代码）
4. 想要 c++ 代码的朋友可以私信我交流。或者下一次写一篇，如果有机会的话（鬼脸jpg）。

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python代码（带注释）

• 代码输入：1、连续帧图片，2、每帧图片的检测结果。(需要数据的可以私信我)
• 代码参考:git地址
• 输出结果以视频形式保存

main.py文件

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


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):
    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):
    w = np.sqrt(x[2] * x[3])
    h = x[2] / w
    if (score == 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)
        # [center_x, center_y, s, r, center_x', center_y', s']  s = w * h  r = w / h  bbox 宽高比保持不变
        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):
        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
        # 根据上一帧航迹的框 预测当前帧的框.
        trks = np.zeros((len(self.trackers), 5))
        to_del = []
        ret = []
        for t, trk in enumerate(trks):
            pos = self.trackers[t].predict()[0]
            trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
            if np.any(np.isnan(pos)):
                to_del.append(t)
        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
        for t in reversed(to_del):
            self.trackers.pop(t)

        # 匈牙利匹配 上一帧预测框与当前帧检测框进行 iou 匹配
        matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.iou_threshold)

        # 如果匹配上 则更新修正当前检测框
        for m in matched:
            self.trackers[m[1]].update(dets[m[0], :])

        # 如果检测框未匹配上，则当作新目标，新起航迹
        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
            # 如果超过self.max_age（3）帧都没有匹配上，则应该去除这个航迹
            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))


if __name__ == '__main__':
    display, video_save = True, True  # 是否show,结果是否存视频
    max_age, min_hits, iou_threshold = 3, 3, 0.3  # sort算法参数
    colours = 255 * np.random.rand(32, 3)  # 随机生产颜色
    video = cv2.VideoWriter("video.mp4", cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), 10,
                            (1920, 1080)) if video_save else None
    mot_tracker = Sort(max_age=max_age, min_hits=min_hits, iou_threshold=iou_threshold)  # 创建sort跟踪器
    seq_dets = np.loadtxt("det.txt", delimiter=',')  # 加载检测txt结果
    for frame in range(int(seq_dets[:, 0].max())):
        frame += 1  # 从1帧开始
        dets = seq_dets[seq_dets[:, 0] == frame, 2:7]
        dets[:, 2:4] += dets[:, 0:2]  # [x1,y1,w,h] to [x1,y1,x2,y2] 左上角x1,y1，w，h ——>左上角x1,y1，右下角x2,y2
        mot_tracker.update(dets)  # kalman 预测与更新
        trackers = mot_tracker.trackers
        image_path = os.path.join(".\\img", '%06d.jpg' % (frame))  # 图片路径
        image = cv2.imread(image_path)
        for d in trackers:
            x1, y1, w, h = d.get_state()[0]  # 获取 当前目标框状态
            id = d.id
            color = colours[int(id) % 32, :]
            color = (int(color[0]), int(color[1]), int(color[2]))
            cv2.rectangle(image, (int(x1), int(y1)), (int(w), int(h)), color, 3)  # 画框
            cv2.putText(image, str(int(id)), (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1,
                        color, 3)  # 画id
        if display:
            cv2.namedWindow("show")
            cv2.imshow("show", image)
            cv2.waitKey(0)
        if video_save:
            video.write(image)

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算法分析

• 优点：速度，快，很快，非常快。1s可以处理超过1000帧检测结果。
• 缺点：对于遮挡、以及非线性运动的物体（加减速或者转弯）跟踪效果差
• 优化方向：优化方式有很多，下次再写博客分享，如果有机会的话[鬼脸.jpg]。