bot-sort多目标跟踪

### Bot-Sort算法概述

Bot-Sort是一种多目标跟踪(MOT)方法，在计算机视觉领域用于处理视频序列中的多个移动对象。该算法通过融合检测器输出和运动模型来估计场景内各个物体的位置、速度和其他属性[^1]。

### 工作原理

核心理念在于利用卡尔曼滤波(Kalman Filter,KF)预测轨迹状态，并采用匈牙利匹配(Hungarian Algorithm)关联新出现的目标框与现有轨迹。具体而言：

- **初始化阶段**：当首次发现新的未被追踪的对象实例时，会为其创建一条独立的轨迹记录。

- **更新过程**：
- 对于每条现存轨迹，基于先前观测到的数据应用KF做一步向前预报；
- 计算当前时刻候选边界盒同各已知路径间的相似度得分矩阵；
- 使用二分图最大加权匹配技术解决分配问题；

- **终止条件**：如果某条轨迹连续丢失超过设定阈值，则认为对应实体已经离开视野范围并停止维护这条链路。

import numpy as np
from filterpy.kalman import KalmanFilter
from scipy.optimize import linear_sum_assignment as linear_assignment

class BOT_SORT():
    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 = []
    
    @staticmethod
    def convert_bbox_to_z(bbox): # bbox=[xmin,ymin,xmax,ymax]
        w = bbox[2]-bbox[0]
        h = bbox[3]-bbox[1]
        x = bbox[0]+w/2.
        y = bbox[1]+h/2.
        return np.array([x,y,w,h]).reshape((4, 1))

    @staticmethod
    def convert_x_to_bbox(x,score=None):
        w = np.maximum(0., x[2])
        h = np.maximum(0., x[3])
        xmin = x[0]-(w/2.)
        ymin = x[1]-(h/2.)
        xmax=xmin+w
        ymax=ymin+h
        if(score==None):
            return np.array([xmin,ymin,xmax,ymax]).reshape((1,4))
        else:
            return np.concatenate(([xmin],[ymin],[xmax],[ymax],[score])).reshape((1,5)) 

    def update(self,dets=np.empty((0,5))):
        trks = np.zeros((len(self.trackers),5))
        to_del = []

        ret = [] 
        for t,trk in enumerate(trks):
            pos = self.convert_x_to_bbox(self.trackers[t].kalman_filter.x)
            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)

        matched, unmatched_dets, unmatched_trks = associate(detections=dets,predictions=trks,iou_threshold=self.iou_threshold)

        for m in matched:
            self.trackers[m[1]].update(dets[m[0]])

        for u_det in unmatched_dets:
            self.trackers.append(KalmanBoxTracker(dets[u_det]))

        i = len(self.trackers)
        for index_trk in unmatched_trks:
            i -= 1
            track = self.trackers[index_trk]
            track.mark_missed()
            
            d = track.get_state()[0]
            if(track.time_since_update<1 and (track.hit_streak>=self.min_hits or self.frame_count<=self.min_hits)):
                ret.append(np.concatenate((d,[track.id+1])).reshape(1,-1)) # +1 as MOT benchmark requires positive
            if(i==0):
                break
                
        if(len(ret)>0):
            return np.concatenate(ret)
        return np.empty((0,5))

此实现展示了如何构建一个简单的BOT SORT框架，其中包含了基本组件如Kalman过滤器的状态转换以及IOU匹配逻辑。实际部署可能还需要考虑更多细节优化性能表现。