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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
- **[Apr 2023]** [CASIA-E](datasets/CASIA-E/README.md) is supported by OpenGait.
- **[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://arxiv.org/pdf/2211.10598), [*Dataset and Code(Coming Soon)*](https://lidargait.github.io).
- [**CVPR 2023 Highlight**] OpenGait: Revisiting Gait Recognition Toward Better Practicality, [*Paper*](https://arxiv.org/pdf/2211.06597.pdf), [*Code*](configs/gaitbase).
- [**ECCV 2022**] GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality, [*Paper*](https://arxiv.org/pdf/2203.03972), [*Code*](configs/gaitedge/README.md).
## A Real Gait Recognition System: All-in-One-Gait
<div align="center">
<img src="./assets/gallery.gif" width = "144" height = "256" alt="gallery" />
<img src="./assets/probe1-After.gif" width = "455" height = "256" alt="probe1-After" />
<img src="./assets/probe2-After.gif" width = "144" height = "256" alt="probe2-After" />
</div>
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.