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πππ **[*OpenGait*](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf) has been accpected by CVPR2023 as a highlight paperοΌ** πππ
OpenGait is a flexible and extensible gait recognition project provided by the [Shiqi Yu Group](https://faculty.sustech.edu.cn/yusq/) and supported in part by [WATRIX.AI](http://www.watrix.ai).
## What's New
- **[May 2023]** A real gait recognition system [All-in-One-Gait](https://github.com/jdyjjj/All-in-One-Gait) provided by [Dongyang Jin](https://github.com/jdyjjj) is avaliable.
- [Apr 2023] [CASIA-E](datasets/CASIA-E/README.md) is supported by OpenGait.
- [Feb 2023] [HID 2023 competition](https://hid2023.iapr-tc4.org/) is open, welcome to participate. Additionally, tutorial for the competition has been updated in [datasets/HID/](./datasets/HID).
- [Dec 2022] Dataset [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark) is supported in [datasets/Gait3D](./datasets/Gait3D).
- [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW).
## Our Publications
- [**CVPR 2023**] LidarGait: Benchmarking 3D Gait Recognition with Point Clouds, [*Paper*](https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.pdf), [*Dataset and Code(Coming Soon)*](https://lidargait.github.io).
- [**CVPR 2023 Highlight**] OpenGait: Revisiting Gait Recognition Toward Better Practicality, [*Paper*](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf), [*Code*](configs/gaitbase).
- [**ECCV 2022**] GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality, [*Paper*](), [*Code*](configs/gaitedge/README.md).
## A Real Gait Recognition System: All-in-One-Gait
The workflow of [All-in-One-Gait](https://github.com/jdyjjj/All-in-One-Gait) involves the processes of pedestrian tracking, segmentation and recognition.
The participants shown in the left video are gallery subjects, and that of other two videos are probe subjects.
The recognition results are represented by the color of the bounding boxes.
## Highlighted features
- **Mutiple Dataset supported**: [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp), [OUMVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html), [HID](http://hid2022.iapr-tc4.org/), [GREW](https://www.grew-benchmark.org), [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark), and [CASIA-E](https://www.scidb.cn/en/detail?dataSetId=57be0e918db743279baf44a38d013a06).
- **Multiple Models Support**: We reproduced several SOTA methods, and reached the same or even the better performance.
- **DDP Support**: The officially recommended [`Distributed Data Parallel (DDP)`](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) mode is used during both the training and testing phases.
- **AMP Support**: The [`Auto Mixed Precision (AMP)`](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html?highlight=amp) option is available.
- **Nice log**: We use [`tensorboard`](https://pytorch.org/docs/stable/tensorboard.html) and `logging` to log everything, which looks pretty.
## Getting Started
Please see [0.get_started.md](docs/0.get_started.md). We also provide the following tutorials for your reference:
- [Prepare dataset](docs/2.prepare_dataset.md)
- [Detailed configuration](docs/3.detailed_config.md)
- [Customize model](docs/4.how_to_create_your_model.md)
- [Advanced usages](docs/5.advanced_usages.md)
## Model Zoo
Results and models are available in the [model zoo](docs/1.model_zoo.md).
## Authors:
**Open Gait Team (OGT)**
- [Chao Fan (ζ¨θΆ
)](https://chaofan996.github.io), 12131100@mail.sustech.edu.cn
- [Chuanfu Shen (ζ²ε·η¦)](https://faculty.sustech.edu.cn/?p=95396&tagid=yusq&cat=2&iscss=1&snapid=1&orderby=date), 11950016@mail.sustech.edu.cn
- [Junhao Liang (ζ’ε³»θ±ͺ)](https://faculty.sustech.edu.cn/?p=95401&tagid=yusq&cat=2&iscss=1&snapid=1&orderby=date), 12132342@mail.sustech.edu.cn
## Acknowledgement
- GLN: [Saihui Hou (δΎ―θ΅θΎ)](http://home.ustc.edu.cn/~saihui/index_english.html)
- GaitGL: [Beibei Lin (ζθ΄θ΄)](https://scholar.google.com/citations?user=KyvHam4AAAAJ&hl=en&oi=ao)
- GREW: [GREW TEAM](https://www.grew-benchmark.org)
## Citation
```
@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.
