diff --git a/deploy/pipeline/docs/tutorials/pphuman_mot.md b/deploy/pipeline/docs/tutorials/pphuman_mot.md
index 6380ffbbd7f68d6feb286a1b4f4f33eebcf473a5..00c35c04d37c38d555c8a625456643dbba4cb5ce 100644
--- a/deploy/pipeline/docs/tutorials/pphuman_mot.md
+++ b/deploy/pipeline/docs/tutorials/pphuman_mot.md
@@ -76,13 +76,16 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
                                                    --region_polygon 200 200 400 200 300 400 100 400
 ```
 **注意:**
+ - 区域闯入的测试视频必须是静止摄像头拍摄的，镜头不能抖动或移动。
  - `--do_break_in_counting`表示是否进行区域出入后计数，不设置即默认为False。
  - `--region_type`表示流量计数的区域，当设置`--do_break_in_counting`时仅可选择`custom`，默认是`custom`，表示以用户自定义区域为出入口，同一物体框的下边界中点坐标在相邻两秒内从区域外到区域内，即完成计数加一。
- - `--region_polygon`表示用户自定义区域的多边形的点坐标序列，每两个为一对点坐标(x,y),按顺时针顺序连成一个封闭区域，至少需要3对点也即6个整数，默认值是`[]`，需要用户自行设置点坐标。用户可以运行[此段代码](../../tools/get_video_info.py)获取所测视频的分辨率帧数，以及可以自定义画出自己想要的多边形区域的可视化并自己调整。
+ - `--region_polygon`表示用户自定义区域的多边形的点坐标序列，每两个为一对点坐标(x,y)，**按顺时针顺序**连成一个**封闭区域**，至少需要3对点也即6个整数，默认值是`[]`，需要用户自行设置点坐标，如是四边形区域，坐标顺序是`左上、右上、右下、左下`。用户可以运行[此段代码](../../tools/get_video_info.py)获取所测视频的分辨率帧数，以及可以自定义画出自己想要的多边形区域的可视化并自己调整。
  自定义多边形区域的可视化代码运行如下：
  ```python
  python get_video_info.py --video_file=demo.mp4 --region_polygon 200 200 400 200 300 400 100 400
  ```
+ 快速画出想要的区域的小技巧：先任意取点得到图片，用画图工具打开，鼠标放到想要的区域点上会显示出坐标，记录下来并取整，作为这段可视化代码的region_polygon参数，并再次运行可视化，微调点坐标参数直至满意。
+
 
 测试效果如下：
 
diff --git a/deploy/pipeline/docs/tutorials/ppvehicle_illegal_parking.md b/deploy/pipeline/docs/tutorials/ppvehicle_illegal_parking.md
index 8fa4a269fbcbbbf2cf570e6dfc12b55693798fd5..c3f87db6c6b5392d4bb5d2a368596fa559293021 100644
--- a/deploy/pipeline/docs/tutorials/ppvehicle_illegal_parking.md
+++ b/deploy/pipeline/docs/tutorials/ppvehicle_illegal_parking.md
@@ -55,6 +55,16 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe
 - region_type：非法停车区域类型，custom表示自定义；
 - region_polygon：自定义非法停车多边形，至少为3个点。
 
+**注意:**
+ - 违法停车的测试视频必须是静止摄像头拍摄的，镜头不能抖动或移动。
+ - `--region_polygon`表示用户自定义区域的多边形的点坐标序列，每两个为一对点坐标(x,y)，**按顺时针顺序**连成一个**封闭区域**，至少需要3对点也即6个整数，默认值是`[]`，需要用户自行设置点坐标，如是四边形区域，坐标顺序是`左上、右上、右下、左下`。用户可以运行[此段代码](../../tools/get_video_info.py)获取所测视频的分辨率帧数，以及可以自定义画出自己想要的多边形区域的可视化并自己调整。
+ 自定义多边形区域的可视化代码运行如下：
+ ```python
+ python get_video_info.py --video_file=demo.mp4 --region_polygon 200 200 400 200 300 400 100 400
+ ```
+ 快速画出想要的区域的小技巧：先任意取点得到图片，用画图工具打开，鼠标放到想要的区域点上会显示出坐标，记录下来并取整，作为这段可视化代码的region_polygon参数，并再次运行可视化，微调点坐标参数直至满意。
+
+
 3. 若修改模型路径，有以下两种方式：
 
     - 方法一：```./deploy/pipeline/config/examples/infer_cfg_illegal_parking.yml```下可以配置不同模型路径；
diff --git a/deploy/pipeline/docs/tutorials/ppvehicle_mot.md b/deploy/pipeline/docs/tutorials/ppvehicle_mot.md
index dafefe4c1543627a5890db6fd0f78cacd9f4af01..73b540d8e79ce6b1defe0bbaca4a5aceed62888a 100644
--- a/deploy/pipeline/docs/tutorials/ppvehicle_mot.md
+++ b/deploy/pipeline/docs/tutorials/ppvehicle_mot.md
@@ -86,13 +86,16 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
                                                    --region_polygon 200 200 400 200 300 400 100 400
 ```
 **注意:**
+ - 区域闯入的测试视频必须是静止摄像头拍摄的，镜头不能抖动或移动。
  - `--do_break_in_counting`表示是否进行区域出入后计数，不设置即默认为False。
  - `--region_type`表示流量计数的区域，当设置`--do_break_in_counting`时仅可选择`custom`，默认是`custom`，表示以用户自定义区域为出入口，同一物体框的下边界中点坐标在相邻两秒内从区域外到区域内，即完成计数加一。
- - `--region_polygon`表示用户自定义区域的多边形的点坐标序列，每两个为一对点坐标(x,y),按顺时针顺序连成一个封闭区域，至少需要3对点也即6个整数，默认值是`[]`，需要用户自行设置点坐标。用户可以运行[此段代码](../../tools/get_video_info.py)获取所测视频的分辨率帧数，以及可以自定义画出自己想要的多边形区域的可视化并自己调整。
+ - `--region_polygon`表示用户自定义区域的多边形的点坐标序列，每两个为一对点坐标(x,y)，**按顺时针顺序**连成一个**封闭区域**，至少需要3对点也即6个整数，默认值是`[]`，需要用户自行设置点坐标，如是四边形区域，坐标顺序是`左上、右上、右下、左下`。用户可以运行[此段代码](../../tools/get_video_info.py)获取所测视频的分辨率帧数，以及可以自定义画出自己想要的多边形区域的可视化并自己调整。
  自定义多边形区域的可视化代码运行如下：
  ```python
  python get_video_info.py --video_file=demo.mp4 --region_polygon 200 200 400 200 300 400 100 400
  ```
+ 快速画出想要的区域的小技巧：先任意取点得到图片，用画图工具打开，鼠标放到想要的区域点上会显示出坐标，记录下来并取整，作为这段可视化代码的region_polygon参数，并再次运行可视化，微调点坐标参数直至满意。
+
 
 【效果展示】
 
diff --git a/deploy/pipeline/pipeline.py b/deploy/pipeline/pipeline.py
index 3f0b8ce4ac4e9b6ee8f978dfe19987d875a5363a..32346843e95ecd1236423b883bdc18cc3d73b3f5 100644
--- a/deploy/pipeline/pipeline.py
+++ b/deploy/pipeline/pipeline.py
@@ -626,10 +626,20 @@ class PipePredictor(object):
                 mot_result = (frame_id + 1, boxes[0], scores[0],
                               ids[0])  # single class
                 statistic = flow_statistic(
-                    mot_result, self.secs_interval, self.do_entrance_counting,
-                    self.do_break_in_counting, self.region_type, video_fps,
-                    entrance, id_set, interval_id_set, in_id_list, out_id_list,
-                    prev_center, records)
+                    mot_result,
+                    self.secs_interval,
+                    self.do_entrance_counting,
+                    self.do_break_in_counting,
+                    self.region_type,
+                    video_fps,
+                    entrance,
+                    id_set,
+                    interval_id_set,
+                    in_id_list,
+                    out_id_list,
+                    prev_center,
+                    records,
+                    ids2names=self.mot_predictor.pred_config.labels)
                 records = statistic['records']
 
                 if self.illegal_parking_time != -1:
diff --git a/deploy/pptracking/python/mot/utils.py b/deploy/pptracking/python/mot/utils.py
index d31fe7d17d7cad467f7177c7af0a9e067d572de3..5b871a57b26f5e26ab528bff0aa263f93d983d18 100644
--- a/deploy/pptracking/python/mot/utils.py
+++ b/deploy/pptracking/python/mot/utils.py
@@ -224,7 +224,7 @@ def flow_statistic(result,
                    prev_center,
                    records,
                    data_type='mot',
-                   num_classes=1):
+                   ids2names=['pedestrian']):
     # Count in/out number: 
     # Note that 'region_type' should be one of ['horizontal', 'vertical', 'custom'],
     # 'horizontal' and 'vertical' means entrance is the center line as the entrance when do_entrance_counting, 
@@ -282,25 +282,27 @@ def flow_statistic(result,
                 frame_id -= 1
             x1, y1, w, h = tlwh
             center_x = min(x1 + w / 2., im_w - 1)
-            center_down_y = min(y1 + h, im_h - 1)
+            if ids2names[0] == 'pedestrian':
+                center_y = min(y1 + h, im_h - 1)
+            else:
+                center_y = min(y1 + h / 2, im_h - 1)
 
             # counting objects in region of the first frame
             if frame_id == 1:
-                if in_quadrangle([center_x, center_down_y], entrance, im_h,
-                                 im_w):
+                if in_quadrangle([center_x, center_y], entrance, im_h, im_w):
                     in_id_list.append(-1)
                 else:
-                    prev_center[track_id] = [center_x, center_down_y]
+                    prev_center[track_id] = [center_x, center_y]
             else:
                 if track_id in prev_center:
                     if not in_quadrangle(prev_center[track_id], entrance, im_h,
                                          im_w) and in_quadrangle(
-                                             [center_x, center_down_y],
-                                             entrance, im_h, im_w):
+                                             [center_x, center_y], entrance,
+                                             im_h, im_w):
                         in_id_list.append(track_id)
-                    prev_center[track_id] = [center_x, center_down_y]
+                    prev_center[track_id] = [center_x, center_y]
                 else:
-                    prev_center[track_id] = [center_x, center_down_y]
+                    prev_center[track_id] = [center_x, center_y]
 
 # Count totol number, number at a manual-setting interval
     frame_id, tlwhs, tscores, track_ids = result
diff --git a/deploy/pptracking/python/mot_jde_infer.py b/deploy/pptracking/python/mot_jde_infer.py
index 0fa63f389ad419729d01c1508a6eeb67f7e594f1..e3a9958f7a79b568d0cb1ba9c649aa0499b21e0b 100644
--- a/deploy/pptracking/python/mot_jde_infer.py
+++ b/deploy/pptracking/python/mot_jde_infer.py
@@ -393,10 +393,21 @@ class JDE_Detector(Detector):
                 result = (frame_id + 1, online_tlwhs[0], online_scores[0],
                           online_ids[0])
                 statistic = flow_statistic(
-                    result, self.secs_interval, self.do_entrance_counting,
-                    self.do_break_in_counting, self.region_type, video_fps,
-                    entrance, id_set, interval_id_set, in_id_list, out_id_list,
-                    prev_center, records, data_type, num_classes)
+                    result,
+                    self.secs_interval,
+                    self.do_entrance_counting,
+                    self.do_break_in_counting,
+                    self.region_type,
+                    video_fps,
+                    entrance,
+                    id_set,
+                    interval_id_set,
+                    in_id_list,
+                    out_id_list,
+                    prev_center,
+                    records,
+                    data_type,
+                    ids2names=self.pred_config.labels)
                 records = statistic['records']
 
             fps = 1. / timer.duration
diff --git a/deploy/pptracking/python/mot_sde_infer.py b/deploy/pptracking/python/mot_sde_infer.py
index 07355a2c444967f1b79489d0888356ea1ab1bc97..d8787ec70efab564ce296f57b82f3933f417d293 100644
--- a/deploy/pptracking/python/mot_sde_infer.py
+++ b/deploy/pptracking/python/mot_sde_infer.py
@@ -634,10 +634,21 @@ class SDE_Detector(Detector):
                 result = (frame_id + 1, online_tlwhs[0], online_scores[0],
                           online_ids[0])
                 statistic = flow_statistic(
-                    result, self.secs_interval, self.do_entrance_counting,
-                    self.do_break_in_counting, self.region_type, video_fps,
-                    entrance, id_set, interval_id_set, in_id_list, out_id_list,
-                    prev_center, records, data_type, num_classes)
+                    result,
+                    self.secs_interval,
+                    self.do_entrance_counting,
+                    self.do_break_in_counting,
+                    self.region_type,
+                    video_fps,
+                    entrance,
+                    id_set,
+                    interval_id_set,
+                    in_id_list,
+                    out_id_list,
+                    prev_center,
+                    records,
+                    data_type,
+                    ids2names=self.pred_config.labels)
                 records = statistic['records']
 
             fps = 1. / timer.duration