OpenGait/README.md

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OpenGait is a flexible and extensible gait recognition project provided by the Shiqi Yu Group and supported in part by WATRIX.AI. The corresponding paper has been accepted by CVPR2023 as a highlight paper.

What's New

• [Jan 2024] The code of transfomer-based SwinGait is available at here.
• [Dec 2023] A new state-of-the-art baseline, i.e., DeepGaitV2, is available at here!
• [Nov 2023] The first million-level unlabeled gait dataset, i.e., GaitLU-1M, is released and supported in datasets/GaitLU-1M.
• [Oct 2023] Several representative pose-based methods are supported in opengait/modeling/models. This feature is mainly inherited from FastPoseGait. Many thanks to the contributors😊.
• [July 2023] CCPG is supported in datasets/CCPG.

Our Publications

• [AAAI'24] SkeletonGait++: Gait Recognition Using Skeleton Maps. Paper, and Code (coming soon).
• [AAAI'24] Cross-Covariate Gait Recognition: A Benchmark. Paper, Dataset, and Code (coming soon).
• [Arxiv'23] Exploring Deep Models for Practical Gait Recognition. Paper, DeepGaitV2, and SwinGait.
• [PAMI'23] Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark, Paper, Dataset, and Code.
• [CVPR'23] LidarGait: Benchmarking 3D Gait Recognition with Point Clouds, Paper, Dataset and Code.
• [CVPR'23] OpenGait: Revisiting Gait Recognition Toward Better Practicality, Highlight Paper, and Code.
• [ECCV'22] GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality, Paper, and Code.

A Real Gait Recognition System: All-in-One-Gait

The workflow of All-in-One-Gait involves the processes of pedestrian tracking, segmentation and recognition. See here for details.

Highlighted features

• Multiple Dataset supported: CASIA-B, OUMVLP, SUSTech1K, HID, GREW, Gait3D, CCPG, CASIA-E, and GaitLU-1M.
• Multiple Models Support: We reproduced several SOTA methods and reached the same or even better performance.
• DDP Support: The officially recommended Distributed Data Parallel (DDP) mode is used during both the training and testing phases.
• AMP Support: The Auto Mixed Precision (AMP) option is available.
• Nice log: We use tensorboard and logging to log everything, which looks pretty.

Getting Started

Please see 0.get_started.md. We also provide the following tutorials for your reference:

• Prepare dataset
• Detailed configuration
• Customize model
• Advanced usages

Model Zoo

Results of appearance-based gait recognition are available here.

Results of pose-based gait recognition are available here.

Authors:

OpenGait Team (OGT)

• Chao Fan (樊超), 12131100@mail.sustech.edu.cn
• Chuanfu Shen (沈川福), 11950016@mail.sustech.edu.cn
• Junhao Liang (梁峻豪), 12132342@mail.sustech.edu.cn

Acknowledgement

• GLN: Saihui Hou (侯赛辉)

• GaitGL: Beibei Lin (林贝贝)

• GREW: GREW TEAM

• FastPoseGait Team: FastPoseGait Team

• Gait3D Team: Gait3D Team

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.