🎉🎉🎉 OpenGait has been accpected by CVPR2023 as a highlight paper！ 🎉🎉🎉
All-in-One-Gait is a sub-project of OpenGait provided by Shiqi Yu Group that develops a gait recognition system.
The workflow of All-in-One-Gait primarily involves the processes of pedestrian tracking, segmentation and recognition.
Users are encougraed to update the gait recognition models with watching the lastest SOTA methods in OpenGait.
The recognition results are represented by the color of the bounding boxes.
git clone https://github.com/jdyjjj/All-in-One-Gait.git
cd All-in-One-Gait
pip install -r requirements.txt
pip install yolox
demo
|——————checkpoints
| └——————bytetrack_model
| └——————gait_model
| └——————seg_model
└——————libs
└——————output
checkpoints
|——————bytetrack_model
| └——————bytetrack_x_mot17.pth.tar
| └——————yolox_x_mix_det.py
|
└——————gait_model
| └——————xxxx.pt
└——————seg_model
└——————human_pp_humansegv2_mobile_192x192_inference_model_with_softmax
cd All-in-One-Gait/OpenGait/demo/checkpoints
mkdir gait_model
cd gait_model
wget https://github.com/ShiqiYu/OpenGait/releases/download/v2.0/pretrained_grew_gaitbase.zip
unzip -j pretrained_grew_gaitbase.zip
cd All-in-One-Gait/OpenGait/demo/checkpoints/bytetrack_model
pip install --upgrade --no-cache-dir gdown
gdown https://drive.google.com/uc?id=1P4mY0Yyd3PPTybgZkjMYhFri88nTmJX5
Alternatively, you can manually download the checkpoint file and put it into the folder of bytetrack_model.
- bytetrack_x_mot17 [google], [baidu(code:ic0i)]
cd All-in-One-Gait/OpenGait/demo/checkpoints
mkdir seg_model
cd seg_model
wget https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv2_mobile_192x192_inference_model_with_softmax.zip
unzip human_pp_humansegv2_mobile_192x192_inference_model_with_softmax.zip
cd All-in-One-Gait/OpenGait
python demo/libs/main.py
All-in-One-Gait mainly consists of three processes, i.e., pedestrian tracking, segmentation, and recognition.
In the main.py, you need to give two video as inputs and specify one as the gallery and other one as the probe to obtain the recognized results.
The return results will be written into the path of All-in-One-Gait/OpenGait/demo/output/Outputvideos/track_vis/{timestamp} in default.
cd All-in-One-Gait/OpenGait/demo/output
output
└——————GaitFeatures: This stores the corresponding gait features
└——————GaitSilhouette: This stores the corresponding gait silhouette images
└——————InputVideos: This is the folder where the input videos are put
| └——————gallery.mp4
| └——————probe1.mp4
| └——————probe2.mp4
| └——————probe3.mp4
| └——————probe4.mp4
└——————OutputVideos
└——————{timestamp}
└——————gallery.mp4
└——————G-gallery_P-probe1.mp4
└——————G-gallery_P-probe2.mp4
└——————G-gallery_P-probe3.mp4
└——————G-gallery_P-probe4.mp4
{timestamp}: Store the result video of tracking here, naming it consistent with the input video. In addition, videos named like G-{gallery_video_name}_P-{probe_video_name}.mp4 are obtained after gait recognition.
OpenGait Team (OGT)
- Dongyang Jin(金冬阳), [email protected]
- Chao Fan (樊超), [email protected]
- Rui Wang(王睿), [email protected]
- Chuanfu Shen (沈川福), [email protected]
- Junhao Liang (梁峻豪), [email protected]
@InProceedings{Fan_2023_CVPR,
author = {Fan, Chao and Liang, Junhao and Shen, Chuanfu and Hou, Saihui and Huang, Yongzhen and Yu, Shiqi},
title = {OpenGait: Revisiting Gait Recognition Towards Better Practicality},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {9707-9716}
}
Note: This code is only used for academic purposes, people cannot use this code for anything that might be considered commercial use.