diff --git a/configs/mot/bytetrack/README_cn.md b/configs/mot/bytetrack/README_cn.md
index 242319c08332eedc66c6a9f72c85e6f7e6731d87..477e72e6822da3336e71ab7e9d5adf21e5c39f3f 100644
--- a/configs/mot/bytetrack/README_cn.md
+++ b/configs/mot/bytetrack/README_cn.md
@@ -86,7 +86,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/reid
 ### 4. 用导出的模型基于Python去预测
 
 ```bash
-python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --tracker_config=tracker_config.yml --video_file={your video name}.mp4 --device=GPU --scaled=True --save_mot_txts
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --tracker_config=deploy/pptracking/python/tracker_config.yml --video_file={your video name}.mp4 --device=GPU --scaled=True --save_mot_txts
 ```
 **注意:**
  - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`(对每个视频保存一个txt)或`--save_mot_txt_per_img`(对每张图片保存一个txt)表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。
diff --git a/deploy/pphuman/docs/mot.md b/deploy/pphuman/docs/mot.md
index 0552c4df2f807d6b3003258be9c7638bed63bbca..acfed453ab3c9becf4254f12d24821caeaa44d36 100644
--- a/deploy/pphuman/docs/mot.md
+++ b/deploy/pphuman/docs/mot.md
@@ -34,9 +34,9 @@ python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
 python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
                                                    --video_file=test_video.mp4 \
                                                    --device=gpu \
-                                                   --model_dir det=ppyoloe/
                                                    --do_entrance_counting \
-                                                   --draw_center_traj
+                                                   --draw_center_traj \
+                                                   --model_dir det=ppyoloe/
 
 ```
 **注意:**
diff --git a/deploy/pphuman/pipeline.py b/deploy/pphuman/pipeline.py
index 8078ada54b38c0bd6d0f9ab22221bbbc13668836..0e67968827d3a2f33af3364def72387353e5eb80 100644
--- a/deploy/pphuman/pipeline.py
+++ b/deploy/pphuman/pipeline.py
@@ -537,8 +537,9 @@ class PipePredictor(object):
                     self.pipe_timer.total_time.end()
                 if self.cfg['visual']:
                     _, _, fps = self.pipe_timer.get_total_time()
-                    im = self.visualize_video(frame, mot_res, frame_id,
-                                              fps)  # visualize
+                    im = self.visualize_video(frame, mot_res, frame_id, fps,
+                                              entrance, records,
+                                              center_traj)  # visualize
                     writer.write(im)
                     if self.file_name is None:  # use camera_id
                         cv2.imshow('PPHuman', im)
diff --git a/deploy/pptracking/python/README.md b/deploy/pptracking/python/README.md
index d5c34cdf56efec0f0dd7686d2127c33e584eaf37..46ff59a317ca08b2ef34197f5502f858189a8080 100644
--- a/deploy/pptracking/python/README.md
+++ b/deploy/pptracking/python/README.md
@@ -89,9 +89,9 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/reid
 wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/mot17_demo.mp4
 
 # 用导出的PPYOLOv2行人检测模型和PPLCNet ReID模型
-python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyolov2_r50vd_dcn_365e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --tracker_config=tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyolov2_r50vd_dcn_365e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --tracker_config=deploy/pptracking/python/tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
 # 或用导出的PPYOLOe行人检测模型和PPLCNet ReID模型
-python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --tracker_config=tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --tracker_config=deploy/pptracking/python/tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
 ```
 
 ### 2.3 用导出的模型基于Python去预测车辆跟踪
@@ -108,10 +108,10 @@ wget https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet_vehicl
 tar -xvf deepsort_pplcnet_vehicle.tar
 
 # 用导出的PicoDet车辆检测模型和PPLCNet车辆ReID模型
-python deploy/pptracking/python/mot_sde_infer.py --model_dir=picodet_l_640_aic21mtmct_vehicle/ --reid_model_dir=deepsort_pplcnet_vehicle/ --tracker_config=tracker_config.yml --device=GPU --threshold=0.5 --video_file={your video}.mp4 --save_mot_txts --save_images
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=picodet_l_640_aic21mtmct_vehicle/ --reid_model_dir=deepsort_pplcnet_vehicle/ --tracker_config=deploy/pptracking/python/tracker_config.yml --device=GPU --threshold=0.5 --video_file={your video}.mp4 --save_mot_txts --save_images
 
 # 用导出的PP-YOLOv2车辆检测模型和PPLCNet车辆ReID模型
-python deploy/pptracking/python/mot_sde_infer.py --model_dir=ppyolov2_r50vd_dcn_365e_aic21mtmct_vehicle/ --reid_model_dir=deepsort_pplcnet_vehicle/ --tracker_config=tracker_config.yml --device=GPU --threshold=0.5 --video_file={your video}.mp4 --save_mot_txts --save_images
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=ppyolov2_r50vd_dcn_365e_aic21mtmct_vehicle/ --reid_model_dir=deepsort_pplcnet_vehicle/ --tracker_config=deploy/pptracking/python/tracker_config.yml --device=GPU --threshold=0.5 --video_file={your video}.mp4 --save_mot_txts --save_images
 ```
 
 **注意:**
@@ -135,10 +135,10 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/dete
 wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/mot17_demo.mp4
 
 # 用导出的PPYOLOe行人检测模型
-python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --tracker_config=tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --tracker_config=deploy/pptracking/python/tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
 
 # 用导出的PPYOLOe行人检测模型和PPLCNet ReID模型
-python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --tracker_config=tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --tracker_config=deploy/pptracking/python/tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
 ```
 **注意:**
  - ByteTrack模型是加载导出的检测器和单独配置的`--tracker_config`文件运行的，为了实时跟踪所以不需要reid模型，`--reid_model_dir`表示reid导出模型的路径，默认为空，加不加具体视效果而定；