更换文档检测模型
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paddle_detection/configs/mot/headtracking21/README.md
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paddle_detection/configs/mot/headtracking21/README.md
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README_cn.md
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paddle_detection/configs/mot/headtracking21/README_cn.md
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[English](README.md) | 简体中文
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# 特色垂类跟踪模型
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## 人头跟踪（Head Tracking)
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现有行人跟踪器对高人群密度场景表现不佳，人头跟踪更适用于密集场景的跟踪。
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[HT-21](https://motchallenge.net/data/Head_Tracking_21)是一个高人群密度拥挤场景的人头跟踪数据集，场景包括不同的光线和环境条件下的拥挤的室内和室外场景，所有序列的帧速率都是25fps。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/22989727/205540742-820984c2-8920-467a-bdde-faea421018c5.gif" width='800'/>
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</div>
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## 模型库
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### FairMOT 和 ByteTrack 在 HT-21 Training Set上的结果
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| 模型 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
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| :--------------| :------- | :----: | :----: | :---: | :----: | :---: | :------: | :----: |:----: |
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| FairMOT DLA-34 | 1088x608 | 64.7 | 69.0 | 8533 | 148817 | 234970 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) |
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| ByteTrack-x | 1440x800 | 64.1 | 63.4 | 4191 | 185162 | 210240 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/bytetrack_yolox_ht21.pdparams) | [配置文件](../bytetrack/bytetrack_yolox_ht21.yml) |
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### FairMOT 和 ByteTrack 在 HT-21 Test Set上的结果
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| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
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| :--------------| :------- | :----: | :----: | :----: | :----: | :----: |:-------: | :----: | :----: |
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| FairMOT DLA-34 | 1088x608 | 60.8 | 62.8 | 12781 | 118109 | 198896 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) |
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| ByteTrack-x | 1440x800 | 72.6 | 61.8 | 5163 | 71235 | 154139 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/bytetrack_yolox_ht21.pdparams) | [配置文件](../bytetrack/bytetrack_yolox_ht21.yml) |
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**注意:**
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- FairMOT DLA-34使用2个GPU进行训练，每个GPU上batch size为6，训练30个epoch。
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- ByteTrack使用YOLOX-x做检测器，使用8个GPU进行训练，每个GPU上batch size为8，训练30个epoch，具体细节参照[bytetrack](../bytetrack/)。
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- 此处提供PaddleDetection团队整理后的[下载链接](https://bj.bcebos.com/v1/paddledet/data/mot/HT21.zip)，下载后需解压放到`dataset/mot/`目录下，HT-21 Test集的结果需要交到[官网](https://motchallenge.net)评测。
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## 快速开始
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### 1. 训练
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使用2个GPU通过如下命令一键式启动训练
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```bash
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python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608_headtracking21/ --gpus 0,1 tools/train.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml
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```
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### 2. 评估
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使用单张GPU通过如下命令一键式启动评估
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```bash
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# 使用PaddleDetection发布的权重
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CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams
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# 使用训练保存的checkpoint
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CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=output/fairmot_dla34_30e_1088x608_headtracking21/model_final.pdparams
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```
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### 3. 预测
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使用单个GPU通过如下命令预测一个视频，并保存为视频
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```bash
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# 预测一个视频
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CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams --video_file={your video name}.mp4 --save_videos
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```
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**注意:**
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- 请先确保已经安装了[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。
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### 4. 导出预测模型
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```bash
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CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams
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```
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### 5. 用导出的模型基于Python去预测
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```bash
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python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_headtracking21 --video_file={your video name}.mp4 --device=GPU --save_mot_txts
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```
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**注意:**
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- 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。
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- 跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,-1,-1,-1`。
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## 引用
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```
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@article{zhang2020fair,
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title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking},
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author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu},
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journal={arXiv preprint arXiv:2004.01888},
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year={2020}
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}
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@InProceedings{Sundararaman_2021_CVPR,
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author = {Sundararaman, Ramana and De Almeida Braga, Cedric and Marchand, Eric and Pettre, Julien},
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title = {Tracking Pedestrian Heads in Dense Crowd},
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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month = {June},
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year = {2021},
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pages = {3865-3875}
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}
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@article{zhang2021bytetrack,
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title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
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author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
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journal={arXiv preprint arXiv:2110.06864},
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year={2021}
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}
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```
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_BASE_: [
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'../fairmot/fairmot_dla34_30e_1088x608.yml'
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]
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weights: output/fairmot_dla34_30e_1088x608_headtracking21/model_final
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# for MOT training
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TrainDataset:
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!MOTDataSet
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dataset_dir: dataset/mot
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image_lists: ['ht21.train']
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data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
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# for MOT evaluation
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# If you want to change the MOT evaluation dataset, please modify 'data_root'
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EvalMOTDataset:
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!MOTImageFolder
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dataset_dir: dataset/mot
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data_root: HT21/images/test
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keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
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# for MOT video inference
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TestMOTDataset:
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!MOTImageFolder
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dataset_dir: dataset/mot
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keep_ori_im: True # set True if save visualization images or video
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