有关SORT的论文早先就已经拜读过了，一直想写这篇文章的源码解析，终于有时间来写了。

论文解读请参考：SIMPLE ONLINE AND REALTIME TRACKING （SORT）论文阅读笔记

论文地址：https://arxiv.org/abs/1602.00763

github地址：https://github.com/abewley/sort

以下用到的图片转自HaoBBNuanMM， 目的是为了更好的分析源码流程。

OK，接下来是源码分析，下载下来的项目代码中有如下内容：

主要就sort代码进行解析~

sort.py

"""
    SORT: A Simple, Online and Realtime Tracker
    Copyright (C) 2016 Alex Bewley alex@dynamicdetection.com
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""
from __future__ import print_function
 
from numba import jit      #是python的一个JIT库，通过装饰器来实现运行时的加速
import os.path
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches  #用于绘制常见图像（如矩形，椭圆，圆形，多边形）
from skimage import io
from sklearn.utils.linear_assignment_ import linear_assignment
import glob
import time
import argparse
from filterpy.kalman import KalmanFilter  #filterpy包含了一些常用滤波器的库
 
@jit  #用了jit装饰器，可加速for循环的计算
def iou(bb_test,bb_gt):
  """
  Computes IOU between two bboxes in the form [x1,y1,x2,y2]
  """
  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])   #IOU=（bb_test和bb_gt框相交部分面积）/(bb_test框面积+bb_gt框面积 - 两者相交面积)
    + (bb_gt[2]-bb_gt[0])*(bb_gt[3]-bb_gt[1]) - wh)
  return(o)
 
def convert_bbox_to_z(bbox): #将bbox由[x1,y1,x2,y2]形式转为 [框中心点x,框中心点y,框面积s,宽高比例r]^T
  """
  Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
    [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
    the aspect ratio
  """
  w = bbox[2]-bbox[0]
  h = bbox[3]-bbox[1]
  x = bbox[0]+w/2.
  y = bbox[1]+h/2.
  s = w*h    #scale is just area
  r = w/float(h)
  return np.array([x,y,s,r]).reshape((4,1))  #将数组转为4行一列形式，即[x,y,s,r]^T
 
def convert_x_to_bbox(x,score=None): #将[x,y,s,r]形式的bbox，转为[x1,y1,x2,y2]形式
  """
  Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
    [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
  """
  w = np.sqrt(x[2]*x[3])  #w=sqrt(w*h * w/h)
  h = x[2]/w              #h=w*h/w
  if(score==None): #如果检测框不带置信度
    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))  #返回[x1,y1,x2,y2]
  else:            #如果加测框带置信度
    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.,score]).reshape((1,5)) #返回[x1,y1,x2,y2,score]
 
 
class KalmanBoxTracker(object):
  """
  This class represents the internel state of individual tracked objects observed as bbox.
  """
  count = 0
  def __init__(self,bbox):
    """
    Initialises a tracker using initial bounding box.  使用初始边界框初始化跟踪器
    """
    #define constant velocity model                #定义匀速模型
    self.kf = KalmanFilter(dim_x=7, dim_z=4)       #状态变量是7维， 观测值是4维的，按照需要的维度构建目标
    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. #give high uncertainty to the unobservable initial velocities 对未观测到的初始速度给出高的不确定性
    self.kf.P *= 10.          # 默认定义的协方差矩阵是np.eye(dim_x)，将P中的数值与10， 1000相乘，赋值不确定性
    self.kf.Q[-1,-1] *= 0.01
    self.kf.Q[4:,4:] *= 0.01
 
    self.kf.x[:4] = convert_bbox_to_z(bbox)  #将bbox转为 [x,y,s,r]^T形式，赋给状态变量X的前4位
    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):
    """
    Updates the state vector with observed 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):
    """
    Advances the state vector and returns the predicted bounding box estimate.
    """
    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):
    """
    Returns the current bounding box estimate.
    """
    return convert_x_to_bbox(self.kf.x)
 
def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):  #用于将检测与跟踪进行关联
  """
  Assigns detections to tracked object (both represented as bounding boxes)
  Returns 3 lists of matches, unmatched_detections and unmatched_trackers
  """
  if(len(trackers)==0):  #如果跟踪器为空
    return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
  iou_matrix = np.zeros((len(detections),len(trackers)),dtype=np.float32) # 检测器与跟踪器IOU矩阵
 
  for d,det in enumerate(detections):
    for t,trk in enumerate(trackers):
      iou_matrix[d,t] = iou(det,trk)   #计算检测器与跟踪器的IOU并赋值给IOU矩阵对应位置
  matched_indices = linear_assignment(-iou_matrix)    # 参考：https://blog.csdn.net/herr_kun/article/details/86509591    加上负号是因为linear_assignment求的是最小代价组合，而我们需要的是IOU最大的组合方式，所以取负号
 
  unmatched_detections = []    #未匹配上的检测器
  for d,det in enumerate(detections):
    if(d not in matched_indices[:,0]):  #如果检测器中第d个检测结果不在匹配结果索引中，则d未匹配上
      unmatched_detections.append(d)
  unmatched_trackers = []      #未匹配上的跟踪器
  for t,trk in enumerate(trackers):
    if(t not in matched_indices[:,1]):  #如果跟踪器中第t个跟踪结果不在匹配结果索引中，则t未匹配上
      unmatched_trackers.append(t)
 
  #filter out matched with low IOU   过滤掉那些IOU较小的匹配对
  matches = []  #存放过滤后的匹配结果
  for m in matched_indices:   #遍历粗匹配结果
    if(iou_matrix[m[0],m[1]]<iou_threshold):   #m[0]是检测器ID， m[1]是跟踪器ID，如它们的IOU小于阈值则将它们视为未匹配成功
      unmatched_detections.append(m[0])
      unmatched_trackers.append(m[1])
    else:
      matches.append(m.reshape(1,2))          #将过滤后的匹配对维度变形成1x2形式
  if(len(matches)==0):           #如果过滤后匹配结果为空，那么返回空的匹配结果
    matches = np.empty((0,2),dtype=int)  
  else:                          #如果过滤后匹配结果非空，则按0轴方向继续添加匹配对
    matches = np.concatenate(matches,axis=0)
 
  return matches, np.array(unmatched_detections), np.array(unmatched_trackers)  #其中跟踪器数组是5列的（最后一列是ID）
 
 
 
class Sort(object):
  def __init__(self,max_age=1,min_hits=3):
    """
    Sets key parameters for SORT
    """
    self.max_age = max_age
    self.min_hits = min_hits
    self.trackers = []
    self.frame_count = 0
 
  def update(self,dets):  #输入的是检测结果[x1,y1,x2,y2,score]形式
    """
    Params:
      dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
    Requires: this method must be called once for each frame even with empty detections.   #每一帧都得调用一次，即便检测结果为空
    Returns the a similar array, where the last column is the object ID.                    #返回相似的数组，最后一列是目标ID
    NOTE: The number of objects returned may differ from the number of detections provided.  #返回的目标数量可能与提供的检测数量不同
    """
    self.frame_count += 1   #帧计数
    #get predicted locations from existing trackers.
    trks = np.zeros((len(self.trackers),5)) # 根据当前所有卡尔曼跟踪器的个数创建二维零矩阵，维度为：卡尔曼跟踪器ID个数x 5 (这5列内容为bbox与ID)
    to_del = []                             #存放待删除
    ret = []                                #存放最后返回的结果
    for t,trk in enumerate(trks):      #循环遍历卡尔曼跟踪器列表
      pos = self.trackers[t].predict()[0]           #用卡尔曼跟踪器t 预测 对应物体在当前帧中的bbox
      trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
      if(np.any(np.isnan(pos))):                     #如果预测的bbox为空，那么将第t个卡尔曼跟踪器删除
        to_del.append(t)
    trks = np.ma.compress_rows(np.ma.masked_invalid(trks))  #将预测为空的卡尔曼跟踪器所在行删除，最后trks中存放的是上一帧中被跟踪的所有物体在当前帧中预测的非空bbox
    for t in reversed(to_del): #对to_del数组进行倒序遍历
      self.trackers.pop(t)   #从跟踪器中删除 to_del中的上一帧跟踪器ID
    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks)  #对传入的检测结果 与 上一帧跟踪物体在当前帧中预测的结果做关联，返回匹配的目标矩阵matched, 新增目标的矩阵unmatched_dets, 离开画面的目标矩阵unmatched_trks
 
    #update matched trackers with assigned detections
    for t,trk in enumerate(self.trackers):    # 对卡尔曼跟踪器做遍历
      if(t not in unmatched_trks):                   #如果上一帧中的t还在当前帧画面中（即不在当前预测的离开画面的矩阵unmatched_trks中）
        d = matched[np.where(matched[:,1]==t)[0],0]  #说明卡尔曼跟踪器t是关联成功的，在matched矩阵中找到与其关联的检测器d
        trk.update(dets[d,:][0])                     #用关联的检测结果d来更新卡尔曼跟踪器（即用后验来更新先验）
 
    #create and initialise new trackers for unmatched detections  #对于新增的未匹配的检测结果，创建并初始化跟踪器
    for i in unmatched_dets:                  #新增目标
        trk = KalmanBoxTracker(dets[i,:])     #将新增的未匹配的检测结果dets[i,:]传入KalmanBoxTracker
        self.trackers.append(trk)             #将新创建和初始化的跟踪器trk 传入trackers
    i = len(self.trackers)
    for trk in reversed(self.trackers):       #对新的卡尔曼跟踪器集进行倒序遍历
        d = trk.get_state()[0]                #获取trk跟踪器的状态 [x1,y1,x2,y2]       
        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)) # +1 as MOT benchmark requires positive
        i -= 1
        #remove dead tracklet
        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))
    
def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='SORT demo')
    parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]',action='store_true')
    args = parser.parse_args()
    return args
 
if __name__ == '__main__':
  # all train
  sequences = ['PETS09-S2L1','TUD-Campus','TUD-Stadtmitte','ETH-Bahnhof','ETH-Sunnyday','ETH-Pedcross2','KITTI-13','KITTI-17','ADL-Rundle-6','ADL-Rundle-8','Venice-2']
  args = parse_args()
  display = args.display
  phase = 'train'
  total_time = 0.0
  total_frames = 0
  colours = np.random.rand(32,3) #used only for display
  if(display):
    if not os.path.exists('mot_benchmark'):
      print('\n\tERROR: mot_benchmark link not found!\n\n    Create a symbolic link to the MOT benchmark\n    (https://motchallenge.net/data/2D_MOT_2015/#download). E.g.:\n\n    $ ln -s /path/to/MOT2015_challenge/2DMOT2015 mot_benchmark\n\n')
      exit()
    plt.ion()  #用于动态绘制显示图像
    fig = plt.figure() 
  
  if not os.path.exists('output'):
    os.makedirs('output')
  
  for seq in sequences:
    mot_tracker = Sort() #create instance of the SORT tracker    创建Sort 跟踪实例
    seq_dets = np.loadtxt('data/%s/det.txt'%(seq),delimiter=',') #load detections    #加载检测结果
    with open('output/%s.txt'%(seq),'w') as out_file:
      print("Processing %s."%(seq))
      for frame in range(int(seq_dets[:,0].max())):   #确定视频序列总帧数，并进行for循环
        frame += 1 #detection and frame numbers begin at 1  #由于视频序列帧数是从1开始的，因此加1
        dets = seq_dets[seq_dets[:,0]==frame,2:7]     #提取检测结果中的[x1,y1,w,h,score]到dets
        dets[:,2:4] += dets[:,0:2] #convert to [x1,y1,w,h] to [x1,y1,x2,y2]   将dets中的第2,3列的数加上第0,1列的数后赋值给2,3列；
        total_frames += 1          #总帧数累计
 
        if(display):        #如果要求显示结果
          ax1 = fig.add_subplot(111, aspect='equal')
          fn = 'mot_benchmark/%s/%s/img1/%06d.jpg'%(phase,seq,frame)   #原图像路径名
          im =io.imread(fn)      #加载图像
          ax1.imshow(im)         #显示图像
          plt.title(seq+' Tracked Targets')
 
        start_time = time.time()
        trackers = mot_tracker.update(dets)  #sort跟踪器更新
        cycle_time = time.time() - start_time  #sort跟踪器耗时
        total_time += cycle_time               #sort跟踪器总共耗费时间
 
        for d in trackers:
          print('%d,%d,%.2f,%.2f,%.2f,%.2f,1,-1,-1,-1'%(frame,d[4],d[0],d[1],d[2]-d[0],d[3]-d[1]),file=out_file) #打印： frame,ID,x1,y1,x2,y2,1,-1,-1,-1
          if(display):             #如果显示，将目标检测框画上
            d = d.astype(np.int32)
            ax1.add_patch(patches.Rectangle((d[0],d[1]),d[2]-d[0],d[3]-d[1],fill=False,lw=3,ec=colours[d[4]%32,:]))
            ax1.set_adjustable('box-forced')
 
        if(display):
          fig.canvas.flush_events()
          plt.draw()
          ax1.cla()
 
  print("Total Tracking took: %.3f for %d frames or %.1f FPS"%(total_time,total_frames,total_frames/total_time))
  if(display):
    print("Note: to get real runtime results run without the option: --display")
  
 
 

源码中是对视频图像序列的检测结果进行离线操作，用的检测器第fasrer-rcnn，检测的结果存放在data/视频名/det.txt中。

参考（这篇文章写得真的不错）：【算法分析】SORT/Deep SORT 物体跟踪算法解析