[MOT] clean mot readme modelzoo (#4605)

* clean mot readme modelzoo

* add pptracking link

configs/mot/deepsort/README_cn.md

@@ -120,7 +120,7 @@ Step 1：导出检测模型
 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/jde_yolov3_darknet53_30e_1088x608_mix.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/jde_yolov3_darknet53_30e_1088x608_mix.pdparams
 
 # 或导出PPYOLOv2行人检测模型
-CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/ppyolov2_r50vd_dcn_365e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/mot/deepsort/ppyolov2_r50vd_dcn_365e_640x640_mot17half.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/ppyolov2_r50vd_dcn_365e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyolov2_r50vd_dcn_365e_640x640_mot17half.pdparams
 ```
 
 Step 2：导出ReID模型

configs/mot/fairmot/README.md

@@ -128,6 +128,15 @@ python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34
 The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.
 
 
+### 6. Using exported MOT and keypoint model for unite python inference
+
+```bash
+python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU
+```
+**Notes:**
+ Keypoint model export tutorial: `configs/keypoint/README.md`.
+
+
 ## Citations
 ```
 @article{zhang2020fair,

configs/mot/fairmot/README_cn.md

@@ -125,6 +125,15 @@ python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34
 **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`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。
 
+### 6. 用导出的跟踪和关键点模型Python联合预测
+
+```bash
+python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU
+```
+**注意:**
+ 关键点模型导出教程请参考`configs/keypoint/README.md`。
+
+
 ## 引用
 ```
 @article{zhang2020fair,

deploy/pptracking/python/README.md

@@ -43,7 +43,7 @@ Step 1：导出检测模型
 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/jde_yolov3_darknet53_30e_1088x608_mix.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/jde_yolov3_darknet53_30e_1088x608_mix.pdparams
 
 # 或导出PPYOLOv2行人检测模型
-CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/ppyolov2_r50vd_dcn_365e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/mot/deepsort/ppyolov2_r50vd_dcn_365e_640x640_mot17half.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/ppyolov2_r50vd_dcn_365e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyolov2_r50vd_dcn_365e_640x640_mot17half.pdparams
 ```
 
 Step 2：导出ReID模型