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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
- **[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).
- [Nov 2022] Paper "[OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://arxiv.org/pdf/2211.06597.pdf)" is available now. And the code will be released as soon as possible.
- [Jul 2022] Our paper "[GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality](configs/gaitedge/README.md)" has been accepted by ECCV 2022.
- [Jun 2022] Paper "[A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://arxiv.org/pdf/2206.13732.pdf)" is available now.
- [Jun 2022] Paper "[Learning Gait Representation from Massive Unlabelled Walking Sequences: A Benchmark](https://arxiv.org/pdf/2206.13964.pdf)" is available now. And the code will be released as soon as possible.
- [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW).
## Our Publications
- [CVPR 2023] LIDAR GAIT: Benchmarking 3D Gait Recognition with Point Clouds, [*Paper*](https://arxiv.org/pdf/2211.10598), [*Dataset and Code*](https://lidargait.github.io).
- [CVPR 2023] 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).
## Highlighted features
- **Mutiple Dataset supported**: OpenGait supports four popular gait datasets: [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/), and [GREW](https://www.grew-benchmark.org).
- **Multiple Models Support**: We reproduced several SOTA methods, and reached the same or even the better performance.