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+# 实时跟踪系统PP-Tracking
+
+PP-Tracking是基于飞桨深度学习框架的业界首个开源实时跟踪系统。针对实际业务的难点痛点,PP-Tracking内置行人车辆跟踪、跨镜头跟踪、多类别跟踪、小目标跟踪及流量计数等能力与产业应用,同时提供可视化开发界面。模型集成多目标跟踪,目标检测,ReID轻量级算法,进一步提升PP-Tracking在服务器端部署性能。同时支持python,C++部署,适配Linux,Nvidia Jetson多平台环境。
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+### 一、快速开始
+
+PP-Tracking提供了简洁的可视化界面,无需开发即可实现多种跟踪功能,可以参考[PP-Tracking可视化界面使用文档]()快速上手体验
+
+### 二、算法介绍
+
+PP-Tracking集成了多目标跟踪,目标检测,ReID轻量级算法,提升跟踪系统实时性能。多目标跟踪算法基于FairMOT进行优化,实现了服务器端轻量级模型,同时基于不同应用场景提供了针对性的预训练模型。
+
+模型训练评估方法请参考[多目标跟踪快速开始](../../configs/mot/README_cn.md#快速开始)
+
+PP-Tracking中提供的多场景预训练模型及导出模型列表如下:
+
+| 场景 | 数据集 | 精度(MOTA) | NX模型预测速度(FPS) | 配置文件 | 模型权重 | 预测部署模型 |
+| :---------:|:--------------- | :-------: | :------: | :------: |:---: | :---: |
+| 行人跟踪 | MOT17 | 65.3 | - | [配置文件](../../configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tar) |
+| 行人小目标跟踪 | VisDrone-pedestrian | 40.5 | -| [配置文件](../../configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.tar) |
+| 车辆跟踪 | BDD100k-vehicle | 32.6 | - | [配置文件](../../configs/mot/vehicle/fairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100kmot_vehicle.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100kmot_vehicle.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100kmot_vehicle.tar) |
+| 车辆小目标跟踪 | VisDrone-vehicle | 39.8 | - | [配置文件](../../configs/mot/vehicle/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_vehicle.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_vehicle.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_vehicle.tar)
+| 多类别跟踪 | BDD100k | - | - | [配置文件]() | [下载链接]() | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100k_mcmot.tar) |
+| 多类别小目标跟踪 | VisDrone | 20.4 | - | [配置文件](../../configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.tar) |
+
+更多跟踪模型请参考[多目标跟踪模型库](../../configs/mot/README_cn.md#模型库)
+
+检测模型使用轻量级特色模型PP-PicoDet,具体请参考[PP-PicoDet文档](../../configs/picodet)
+
+ReID模型使用超轻量骨干网络模型PP-LCNet, 具体请参考[PP-LCNet模型介绍](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/zh_CN/models/PP-LCNet.md)
+
+### 三、Python端预测部署
+
+PP-Tracking 使用python预测部署教程请参考[PP-Tracking python部署文档](python/README.md)
+
+### 四、C++端预测部署
+
+PP-Tracking 使用c++预测部署教程请参考[PP-Tracking c++部署文档](cpp/README.md)
diff --git a/deploy/pptracking/python/README.md b/deploy/pptracking/python/README.md
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+++ b/deploy/pptracking/python/README.md
@@ -86,6 +86,8 @@ python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/pp
| --enable_mkldnn | Option | CPU预测中是否开启MKLDNN加速,默认为False |
| --cpu_threads | Option| 设置cpu线程数,默认为1 |
| --trt_calib_mode | Option| TensorRT是否使用校准功能,默认为False。使用TensorRT的int8功能时,需设置为True,使用PaddleSlim量化后的模型时需要设置为False |
+| --do_entrance_counting | Option | 是否统计出入口流量,默认为False |
+| --draw_center_traj | Option | 是否绘制跟踪轨迹,默认为False |
说明:
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