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README_en.md
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......@@ -306,7 +306,7 @@ Contributions are highly welcomed and we would really appreciate your feedback!!
- Thanks [FL77N](https://github.com/FL77N/) for contributing the code of `Sparse-RCNN` model.
- Thanks [Chen-Song](https://github.com/Chen-Song) for contributing the code of `Swin Faster-RCNN` model.
- Thanks [yangyudong](https://github.com/yangyudong2020), [hchhtc123](https://github.com/hchhtc123) for contributing PP-Tracking GUI interface.
- 感谢[Shigure19](https://github.com/Shigure19) for contributing PP-TinyPose fitness APP.
- Thanks [Shigure19](https://github.com/Shigure19) for contributing PP-TinyPose fitness APP.
## Citation
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deploy/pptracking/README.md 已删除 100644 → 0
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# 实时多目标跟踪系统PP-Tracking
PP-Tracking是基于PaddlePaddle深度学习框架的业界首个开源的实时多目标跟踪系统，具有模型丰富、应用广泛和部署高效三大优势。
PP-Tracking支持单镜头跟踪(MOT)和跨镜头跟踪(MTMCT)两种模式，针对实际业务的难点和痛点，提供了行人跟踪、车辆跟踪、多类别跟踪、小目标跟踪、流量统计以及跨镜头跟踪等各种多目标跟踪功能和应用，部署方式支持API调用和GUI可视化界面，部署语言支持Python和C++，部署平台环境支持Linux、NVIDIA Jetson等。
<div width="1000" align="center">
<img src="../../docs/images/pptracking.png"/>
</div>
<div width="1000" align="center">
<img src="../../docs/images/pptracking-demo.gif"/>
<br>
视频来源：VisDrone和BDD100K公开数据集</div>
</div>
## 一、快速开始
### AI Studio公开项目案例
PP-Tracking 提供了AI Studio公开项目案例，教程请参考[PP-Tracking之手把手玩转多目标跟踪](https://aistudio.baidu.com/aistudio/projectdetail/3022582)。
### Python端预测部署
PP-Tracking 支持Python预测部署，教程请参考[PP-Tracking Python部署文档](python/README.md)。
### C++端预测部署
PP-Tracking 支持C++预测部署，教程请参考[PP-Tracking C++部署文档](cpp/README.md)。
### GUI可视化界面预测部署
PP-Tracking 提供了简洁的GUI可视化界面，教程请参考[PP-Tracking可视化界面试用版使用文档](https://github.com/yangyudong2020/PP-Tracking_GUi)。
（感谢@[yangyudong2020](https://github.com/yangyudong2020)、@[hchhtc123](https://github.com/hchhtc123)对飞桨开源的贡献）
## 二、算法介绍
PP-Tracking 支持单镜头跟踪(MOT)和跨镜头跟踪(MTMCT)两种模式。
- 单镜头跟踪同时支持**FairMOT**和**DeepSORT**两种多目标跟踪算法，跨镜头跟踪只支持**DeepSORT**算法。
- 单镜头跟踪的功能包括行人跟踪、车辆跟踪、多类别跟踪、小目标跟踪以及流量统计，模型主要是基于FairMOT进行优化，实现了实时跟踪的效果，同时基于不同应用场景提供了针对性的预训练模型。
- DeepSORT算法方案(包括跨镜头跟踪用到的DeepSORT)，选用的检测器是PaddleDetection自研的高性能检测模型[PP-YOLOv2](../../ppyolo/)和轻量级特色检测模型[PP-PicoDet](../../picodet/)，选用的ReID模型是PaddleClas自研的超轻量骨干网络模型[PP-LCNet](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/zh_CN/models/PP-LCNet.md)
PP-Tracking中提供的多场景预训练模型以及导出后的预测部署模型如下：
| 场景 | 数据集 | 精度（MOTA） | 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 | 23.9 | [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 | 8.35 | [配置文件](../../configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_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 | 24.3 | [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 | 22.8 | [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 | - | 12.5 | [配置文件](../../configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100k_mcmot.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100k_mcmot.pdparams) | [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 | 6.74 | [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_1088x608_visdrone.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.tar) |
**注意：**
1. 模型预测速度的设备为**NVIDIA Jetson Xavier NX**，速度为**TensorRT FP16**速度，测试环境为CUDA 10.2、JETPACK 4.5.1、TensorRT 7.1。
2. 模型权重是指使用PaddleDetection训练完直接保存的权重，更多跟踪模型权重请参考[多目标跟踪模型库](../../configs/mot/README.md#模型库)去下载，也可按照相应模型配置文件去训练。
3. 预测部署模型是指导出后的前向参数的模型，因为PP-Tracking项目的部署过程中只需要前向参数，可根据[多目标跟踪模型库](../../configs/mot/README.md#模型库)去下载并导出，也可按照相应模型配置文件去训练并导出。导出后的模型文件夹应包括`infer_cfg.yml`、`model.pdiparams`、`model.pdiparams.info`和`model.pdmodel`四个文件，一般会将它们以tar格式打包。
## 引用
```
@ARTICLE{9573394,
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Detection and Tracking Meet Drones Challenge},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TPAMI.2021.3119563}
}
@InProceedings{bdd100k,
author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen,
Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
@article{zhang2020fair,
title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking},
author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu},
journal={arXiv preprint arXiv:2004.01888},
year={2020}
}
@inproceedings{Wojke2018deep,
title={Deep Cosine Metric Learning for Person Re-identification},
author={Wojke, Nicolai and Bewley, Alex},
booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2018},
pages={748--756},
organization={IEEE},
doi={10.1109/WACV.2018.00087}
}
```
deploy/pptracking/README.md 0 → 120000
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README_en.md
\ No newline at end of file
deploy/pptracking/README_cn.md 0 → 100644
浏览文件 @ 3c78716d
[English](README_en.md) | 简体中文
# 实时多目标跟踪系统PP-Tracking
PP-Tracking是基于PaddlePaddle深度学习框架的业界首个开源的实时多目标跟踪系统，具有模型丰富、应用广泛和部署高效三大优势。
PP-Tracking支持单镜头跟踪(MOT)和跨镜头跟踪(MTMCT)两种模式，针对实际业务的难点和痛点，提供了行人跟踪、车辆跟踪、多类别跟踪、小目标跟踪、流量统计以及跨镜头跟踪等各种多目标跟踪功能和应用，部署方式支持API调用和GUI可视化界面，部署语言支持Python和C++，部署平台环境支持Linux、NVIDIA Jetson等。
<div width="1000" align="center">
<img src="../../docs/images/pptracking.png"/>
</div>
<div width="1000" align="center">
<img src="../../docs/images/pptracking-demo.gif"/>
<br>
视频来源：VisDrone和BDD100K公开数据集</div>
</div>
## 一、快速开始
### AI Studio公开项目案例
PP-Tracking 提供了AI Studio公开项目案例，教程请参考[PP-Tracking之手把手玩转多目标跟踪](https://aistudio.baidu.com/aistudio/projectdetail/3022582)。
### Python端预测部署
PP-Tracking 支持Python预测部署，教程请参考[PP-Tracking Python部署文档](python/README.md)。
### C++端预测部署
PP-Tracking 支持C++预测部署，教程请参考[PP-Tracking C++部署文档](cpp/README.md)。
### GUI可视化界面预测部署
PP-Tracking 提供了简洁的GUI可视化界面，教程请参考[PP-Tracking可视化界面试用版使用文档](https://github.com/yangyudong2020/PP-Tracking_GUi)。
## 二、算法介绍
PP-Tracking 支持单镜头跟踪(MOT)和跨镜头跟踪(MTMCT)两种模式。
- 单镜头跟踪同时支持**FairMOT**和**DeepSORT**两种多目标跟踪算法，跨镜头跟踪只支持**DeepSORT**算法。
- 单镜头跟踪的功能包括行人跟踪、车辆跟踪、多类别跟踪、小目标跟踪以及流量统计，模型主要是基于FairMOT进行优化，实现了实时跟踪的效果，同时基于不同应用场景提供了针对性的预训练模型。
- DeepSORT算法方案(包括跨镜头跟踪用到的DeepSORT)，选用的检测器是PaddleDetection自研的高性能检测模型[PP-YOLOv2](../../ppyolo/)和轻量级特色检测模型[PP-PicoDet](../../picodet/)，选用的ReID模型是PaddleClas自研的超轻量骨干网络模型[PP-LCNet](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/zh_CN/models/PP-LCNet.md)
PP-Tracking中提供的多场景预训练模型以及导出后的预测部署模型如下：
| 场景 | 数据集 | 精度（MOTA） | 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 | 23.9 | [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 | 8.35 | [配置文件](../../configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_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 | 24.3 | [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 | 22.8 | [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 | - | 12.5 | [配置文件](../../configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100k_mcmot.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100k_mcmot.pdparams) | [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 | 6.74 | [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_1088x608_visdrone.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.tar) |
**注意：**
1. 模型预测速度的设备为**NVIDIA Jetson Xavier NX**，速度为**TensorRT FP16**速度，测试环境为CUDA 10.2、JETPACK 4.5.1、TensorRT 7.1。
2. 模型权重是指使用PaddleDetection训练完直接保存的权重，更多跟踪模型权重请参考[多目标跟踪模型库](../../configs/mot/README.md#模型库)去下载，也可按照相应模型配置文件去训练。
3. 预测部署模型是指导出后的前向参数的模型，因为PP-Tracking项目的部署过程中只需要前向参数，可根据[多目标跟踪模型库](../../configs/mot/README.md#模型库)去下载并导出，也可按照相应模型配置文件去训练并导出。导出后的模型文件夹应包括`infer_cfg.yml`、`model.pdiparams`、`model.pdiparams.info`和`model.pdmodel`四个文件，一般会将它们以tar格式打包。
## 引用
```
@ARTICLE{9573394,
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Detection and Tracking Meet Drones Challenge},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TPAMI.2021.3119563}
}
@InProceedings{bdd100k,
author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen,
Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
@article{zhang2020fair,
title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking},
author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu},
journal={arXiv preprint arXiv:2004.01888},
year={2020}
}
@inproceedings{Wojke2018deep,
title={Deep Cosine Metric Learning for Person Re-identification},
author={Wojke, Nicolai and Bewley, Alex},
booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2018},
pages={748--756},
organization={IEEE},
doi={10.1109/WACV.2018.00087}
}
```
deploy/pptracking/README_en.md
浏览文件 @ 3c78716d
English | [简体中文](README_cn.md)
# Real time Multi-Object Tracking system PP-Tracking
PP-Tracking is the first open source real-time Multi-Object Tracking system, and it is based on PaddlePaddle deep learning framework. It has rich models, wide application and high efficiency deployment.
......
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