1. 动机

        目标跟踪是计算机视觉领域一种常用的算法，用于将前后帧中的同一个目标关联起来，从而可以针对某一个特定目标进行分析，如对状态进行投票平滑获取更为稳健的结果。

        然而，目前流行的跟踪算法大多是基于检测的bbox之间的IOU来匹配的，这对于某些小目标或者点的检测，IOU通常不是一个好的选择，因为目标太小，很容易使得相邻两帧之间的IOU为0。

        为了解决这个问题，本文提出了一种基于点之间距离的跟踪方法：将目标建模为一个点，通过计算前后帧点之间的距离，利用匈牙利匹配来进行跟踪。

2. 方法

        直接上代码，里面给出了跟踪方法的定义以及一个使用示例：

"""
Test for multi-target tracker
"""

import numpy as np
from scipy.optimize import linear_sum_assignment


# 定义目标类
class Target:
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.id = None
        self.miss_num = 0
        self.lost = False  # 添加一个标记来表示目标是否丢失


# 定义跟踪器类
class Tracker:
    def __init__(self, max_age=1, thres_dist=100):
        self.max_age = max_age
        self.thres_dist = thres_dist
        self.tracked_targets = []
        self.last_id = 0

    # 在下一帧中更新目标位置并使用匈牙利算法进行匹配
    def update(self, targets):
        # print([[t.x, t.y] for t in targets])
        # print([[t.x, t.y] for t in self.tracked_targets])
        # 计算每个跟踪器与目标之间的相似度（这里使用欧氏距离作为相似度指标）
        distances = []
        for target in targets:
            dist_list = []
            for tracked_target in self.tracked_targets:
                if not tracked_target.lost:  # 只考虑未丢失的目标
                    dist = np.sqrt((target[0] - tracked_target.x) ** 2 + (target[1] - tracked_target.y) ** 2)
                    dist_list.append(dist)
                else:
                    dist_list.append(np.inf)  # 将丢失的目标设置为无穷大距离，避免被匹配
            distances.append(dist_list)
        distances = np.asarray(distances)
        # 使用匈牙利算法进行匹配
        row_ind, col_ind = linear_sum_assignment(distances)
        # print(row_ind, col_ind, distances)
        # 更新匹配成功的目标位置
        # print(len(self.tracked_targets))
        for i, j in zip(row_ind, col_ind):
            if not self.tracked_targets[j].lost and distances[i][j] < self.thres_dist:  # 只更新未丢失的目标位置
                self.tracked_targets[j].x = targets[i][0]
                self.tracked_targets[j].y = targets[i][1]
            else:
                # 如果目标丢失，继续标记为丢失状态，不进行位置更新，同时累计丢失次数
                self.tracked_targets[j].miss_num += 1
                if self.tracked_targets[j].miss_num >= self.max_age:
                    # print("lost", self.tracked_targets[j].id)
                    self.tracked_targets[j].lost = True
        # 添加新的目标到跟踪列表
        for j in range(len(targets)):
            if j not in col_ind:
                self.create_target(targets[j][0], targets[j][1])
        # 检测丢失的目标，如果目标丢失超过一定帧数，将其从跟踪列表中删除
        lost_targets = []
        for target in self.tracked_targets:
            if target.lost:
                lost_targets.append(target)
        for target in lost_targets:
            self.tracked_targets.remove(target)

    def create_target(self, x, y):
        tar = Target(x, y)
        tar.id = self.last_id
        self.tracked_targets.append(tar)
        self.last_id += 1


def get_color_by_id(id):
    colors = [
        (255, 0, 0),
        (255, 255, 0),
        (255, 0, 255),
        (30, 140, 100),
        (0, 255, 0),
        (0, 50, 200),
        (100, 0, 30),
        (100, 100, 0),
        (20, 10, 200),
        (20, 250, 100),
        (145, 0, 90),
        (15, 10, 190),
        (15, 100, 100),
    ]
    index = id % len(colors)
    return colors[index]


if __name__ == '__main__':
    import cv2
    from PIL import Image
    import copy

    img = np.ones([500, 500, 3]).astype(np.uint8)
    pil_imgs = [Image.fromarray(img)]

    # 初始化目标列表
    preds = [[1, 1], [1, 499], [499, 1], [499, 499]]

    # 初始化跟踪器
    tracker = Tracker()

    # 模拟多帧跟踪过程
    for i in range(100):
        # 在每一帧中，随机移动每个目标的位置
        delta = 10
        if i in [10, 11, 12, 13]:
            delta = 30

        # print([[t.x, t.y] for t in tracker.tracked_targets])
        preds[0][0] += np.random.randint(0, delta)
        preds[0][1] += np.random.randint(0, delta)
        preds[1][0] += np.random.randint(0, delta)
        preds[1][1] -= np.random.randint(0, delta)
        preds[2][0] -= np.random.randint(0, delta)
        preds[2][1] += np.random.randint(0, delta)
        preds[3][0] -= np.random.randint(0, delta)
        preds[3][1] -= np.random.randint(0, delta)
        # print([[t.x, t.y] for t in tracker.tracked_targets])

        # 更新跟踪器的目标列表
        tracker.update(copy.deepcopy(preds))
        # 打印当前帧中每个跟踪到的目标位置
        # print("frame: {}".format(i))
        points, ids = [], []
        for target in tracker.tracked_targets:
            print("Target {}: ({}, {}); lost: {}".format(target.id, target.x, target.y, target.lost))
            points.append([target.x, target.y])
            ids.append(target.id)
            # cv2.circle(img, [target.x, target.y], point_size, point_color, thickness)
            cv2.putText(img, str(target.id), [target.x, target.y], cv2.FONT_HERSHEY_SIMPLEX, 0.4,
                        get_color_by_id(target.id), thickness=1, lineType=cv2.LINE_AA)

        pil_imgs.append(Image.fromarray(img))
        cv2.imshow('img', img)
        if cv2.waitKey(30) == ord('q'):
            break

    # 创建并保存GIF文件
    image_0 = pil_imgs[0]
    image_0.save('track_result.gif', save_all=True, append_images=pil_imgs[1:], duration=30, loop=0)

运行上述代码后，会保存一个GIF文件，展示了多个目标的跟踪结果，如下图：