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diff --git "a/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/mot_benchmark/MY_TEST/www3/996.txt" "b/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/mot_benchmark/MY_TEST/www3/996.txt"
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diff --git "a/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/mot_benchmark/MY_TEST/www3/998.txt" "b/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/mot_benchmark/MY_TEST/www3/998.txt"
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diff --git "a/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/mot_benchmark/MY_TEST/www3/999.txt" "b/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/mot_benchmark/MY_TEST/www3/999.txt"
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diff --git "a/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/obj_tracker.md" "b/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/obj_tracker.md"
new file mode 100755
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@@ -0,0 +1,180 @@
+
+
+# 目标跟踪（track by detection）
+
+![](pic/tracker.gif)
+
+在视频分析（或视频结构化）应用开发中，多目标跟踪是非常重要的一个环节。它能有效弥补上一个目标检测环节中算法的不足，如检测算法输出坐标不稳定、漏检等。与此同时，跟踪算法输出的目标轨迹（track-id）对于应用下阶段的行为分析环节也有着至关重要的作用。下面是常见视频分析类应用系统结构：
+
+![](pic/video_process.png)
+
+目标检测算法输出单帧检测结果，目标跟踪算法负责将前后2帧中的目标关联起来、给予唯一标识track-id。假设t帧中检测到了M个目标，t+1帧中检测到了N个目标，跟踪算法本质上是M->N的匹配关联过程。
+![](pic/t_and_t+1_match.png)
+
+匹配过程中，目标可以分为以下三大类：
+1. `matched_tracks`，t帧目标出现，t+1帧该目标仍然出现，算法匹配上。
+2. `unmatched_tracks`，t帧目标出现，t+1帧该目标消失，算法未匹配上。
+3. `unmatched_detections`，t帧目标不存在，t+1帧该目标出现，新增检测目标。
+
+其中，对于2和3来说，跟踪算法需要考虑：
+> t帧目标出现，t+1帧目标其实仍然存在，但是检测算法出现短暂漏检，误认为其消失。此时的解决方案是： 某帧未被匹配到的tracks不要立即清除，而是做若干帧的缓存，等待若干帧后检测算法恢复检测<br>
+> t帧目标不存在，t+1帧该目标仍然不存在，但是检测算法出现短暂误检，误认为其出现。此时的解决方案是：新增的检测目标不要立即生效，而是做若干帧的缓存，等检测算法连续检测超过若干帧、并且都能匹配关联上后再生效
+
+之所以要考虑以上2点，主要原因是对于连续视频帧而言，大部分检测算法基本无法做到100%连续、稳定检测，出现短暂的误检、漏检非常正常。
+
+<br>
+
+## 题目上下文说明
+
+现假设有以下跟踪代码，
+
+```
+# 定义跟踪算法类
+class Tracker(object):
+  # 初始化参数
+  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
+
+  # 跟踪函数，每帧检测结果返回后，调用一次update
+  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(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)
+    
+    # 匹配关联
+    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.iou_threshold)
+
+    # 后处理逻辑
+    # TO-DO your code...
+
+    # 返回跟踪结果 [[left, top, right, bottom, track-id]...]
+    if(len(ret) > 0):
+      return np.concatenate(ret)
+    return np.empty((0, 5))
+```
+
+其中：
+1. `self.max_age`代表跟踪算法允许出现的最大漏检帧数
+2. `self.min_hints`代表跟踪算法要求的最低连续匹配帧数
+3. `self.trackers`代表跟踪算法维持的目标集合（已生成track-id）
+4. `update(self, dets)`代表跟踪函数，其中参数`dets`代表t+1帧中目标检测结果list[[left, top, right, bottom, score]...]，即t+1帧中待匹配的detections
+5. `associate_detections_to_trackers(...)` 代表IOU+卡尔曼滤波匹配算法，返回上面提到的`matched_tracks`，`unmatched_tracks`，`unmatched_detections`三个值
+6. `time_since_update`代表目标当前漏检帧数
+7. `hit_streak`代表目标当前连续匹配帧数
+
+请你根据上面陈述补充TO-DO部分代码。
+
+
+<br>
+
+## 正确答案
+
+```
+    # 更新matched_tracks
+    for m in matched:
+      self.trackers[m[1]].update(dets[m[0], :])
+
+    # 初始化unmatched_detections，假设是当前帧新出现的检测目标
+    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]
+
+        # 输出满足条件的tracks
+        if (trk.time_since_update <= self.max_age) 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
+        # 移除超过self.max_age次的漏检目标
+        if(trk.time_since_update > self.max_age):
+          self.trackers.pop(i)
+```
+
+
+## 未考虑unmatched_detections
+```
+    # 更新matched_tracks
+    for m in matched:
+      self.trackers[m[1]].update(dets[m[0], :])
+
+    i = len(self.trackers)
+    for trk in reversed(self.trackers):
+        d = trk.get_state()[0]
+        
+        # 输出满足条件的tracks
+        if (trk.time_since_update <= self.max_age) 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
+        # 移除超过self.max_age次的漏检目标
+        if(trk.time_since_update > self.max_age):
+          self.trackers.pop(i)
+```
+
+
+## 未考虑移除长时间未检测到的缓存数据
+
+```
+    # 更新matched_tracks
+    for m in matched:
+      self.trackers[m[1]].update(dets[m[0], :])
+
+    # 初始化unmatched_detections，假设是当前帧新出现的检测目标
+    for i in unmatched_dets:
+        trk = KalmanBoxTracker(dets[i,:])
+        self.trackers.append(trk)
+    
+    for trk in reversed(self.trackers):
+        d = trk.get_state()[0]
+
+        # 输出满足条件的tracks
+        if (trk.time_since_update <= self.max_age) 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))
+```
+
+
+## 未考虑需要连续检出并匹配self.min_hints次才生效
+
+```
+    # 更新matched_tracks
+    for m in matched:
+      self.trackers[m[1]].update(dets[m[0], :])
+
+    # 初始化unmatched_detections，假设是当前帧新出现的检测目标
+    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]
+
+        # 输出满足条件的tracks
+        if (trk.time_since_update <= self.max_age):
+          ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1))
+        
+        i -= 1
+        # 移除超过self.max_age次的漏检目标
+        if(trk.time_since_update > self.max_age):
+          self.trackers.pop(i)
+```
+
diff --git "a/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/obj_tracker.py" "b/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/obj_tracker.py"
new file mode 100755
index 0000000000000000000000000000000000000000..d736da833e96db88b54045ac7a461671fa2b9f47
--- /dev/null
+++ "b/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/obj_tracker.py"
@@ -0,0 +1,248 @@
+"""
+    SORT: A Simple, Online and Realtime Tracker
+    Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
+
+    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
+
+import numpy as np
+import glob
+import time
+
+from filterpy.kalman import KalmanFilter
+
+np.random.seed(0)
+
+
+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):
+  """
+  From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
+  """
+  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):
+  """
+  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))
+
+
+def convert_x_to_bbox(x,score=None):
+  """
+  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])
+  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):
+  """
+  This class represents the internal state of individual tracked objects observed as bbox.
+  """
+  count = 0
+  def __init__(self,bbox):
+    """
+    Initialises a tracker using initial bounding box.
+    """
+    #define constant velocity model
+    self.kf = KalmanFilter(dim_x=7, dim_z=4) 
+    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:] *= 10000. #give high uncertainty to the unobservable initial velocities
+    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):
+    """
+    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
+      print('self.id: {0} self.time_since_update: {1}'.format(self.id + 1, self.time_since_update))
+    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):
+  """
+  Assigns detections to tracked object (both represented as bounding boxes)
+
+  Returns 3 lists of matches, unmatched_detections and unmatched_trackers
+  """
+  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)
+
+  #filter out matched with low IOU
+  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):
+    """
+    Sets key parameters for SORT
+    """
+    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))):
+    """
+    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 (use np.empty((0, 5)) for frames without detections).
+    Returns the a similar array, where the last column is the object ID.
+
+    NOTE: The number of objects returned may differ from the number of detections provided.
+    """
+    self.frame_count += 1
+    # get predicted locations from existing trackers.
+    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)
+    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks, self.iou_threshold)
+
+    # update matched trackers with assigned detections
+    for m in matched:
+      self.trackers[m[1]].update(dets[m[0], :])
+
+    # create and initialise new trackers for unmatched detections
+    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 <= self.max_age) 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):
+          print('remove dead tracklet: {}'.format(trk.id + 1))
+          self.trackers.pop(i)
+    if(len(ret)>0):
+      return np.concatenate(ret)
+    return np.empty((0,5))
diff --git "a/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/obj_tracker_run.py" "b/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/obj_tracker_run.py"
new file mode 100755
index 0000000000000000000000000000000000000000..d2128a8924fae8599765b1c09e210cdbb8ead483
--- /dev/null
+++ "b/data/1.OpenCV\345\210\235\351\230\266/6.\350\247\206\351\242\221\345\210\206\346\236\220/2.\347\233\256\346\240\207\350\267\237\350\270\252/obj_tracker_run.py"
@@ -0,0 +1,94 @@
+
+import os
+import numpy as np
+import time
+
+import cv2
+import operator
+
+from obj_tracker import Sort
+
+scale = 2
+total_time = 0.0
+total_frames = 0
+
+pre_det = []
+tracks = np.empty((0, 5))
+
+# create sort tracker
+sort = Sort(5, 0, 0.2)
+
+# do track and render on image
+def track_and_render(detections, img):
+    global total_frames
+    global total_time
+    global pre_det
+    global tracks
+
+    total_frames += 1
+    start_time = time.time()
+
+    # call the tracker algo
+    if not operator.eq(pre_det, detections):
+        tracks = sort.update(np.array(detections))
+        pre_det = detections
+    else:
+        # if skip frame in detection process
+        tracks = sort.update()
+    
+    # get the fps, calculate avg speed in 100 frames
+    cycle_time = time.time() - start_time
+    total_time += cycle_time
+    if total_frames % 100 == 0:
+        print("Total Tracking took: %.3f seconds for %d frames or %.1f FPS" % (total_time, total_frames, total_frames / total_time))
+        total_time = 0.0
+        total_frames = 0
+    
+    # render on frame image
+    for track in tracks: 
+        # [left, top, right, bottom, id]
+        pt1 = (int(track[0]), int(track[1]))
+        pt2 = (int(track[2]), int(track[3]))
+        cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
+
+        cv2.putText(img, str(int(track[4])),
+                    (pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
+                    (0, 255, 255), 1) 
+
+    # return frame image to caller
+    return img
+
+
+# main
+if __name__ == '__main__':
+    
+    # detection data, generated by object-detector algo model, including frame image
+    path = 'mot_benchmark\\MY_TEST\\www3'
+
+    # read test data from disk in a loop one frame by frame and send it to sort tracker
+    for i in range(400000):
+        # read frame image
+        bg = cv2.imread(path + '\\' + str(i % 800 + 1) + '.jpg')
+        # rescale
+        bg = cv2.resize(bg, (int(1280 / scale), int(720 / scale)))
+        
+        # read detection data in current frame
+        dets = []
+        with open(path + '\\' + str(i % 800 + 1) + '.txt') as txt:
+            lines = txt.readlines()
+            for line in lines:
+                items = line.split(' ')
+                left = float(items[1])
+                top = float(items[2])
+                right = float(items[3])
+                bottom = float(items[4]) 
+                
+                dets.append((left / scale, top / scale, right / scale, bottom / scale, int(items[0])))
+        
+        # send to the tracker and draw the result on background image
+        if len(dets) > 0:
+            bg = track_and_render(dets, bg)
+            cv2.imshow('tracker', bg)
+            cv2.waitKey(40)
+    
+    cv2.destroyAllWindows()
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