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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.

What's New

• [Jul 2022] Our paper "GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality" has been accepted by ECCV 2022.
• [Jun 2022] Paper "A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges" is available now.
• [Jun 2022] Paper "Learning Gait Representation from Massive Unlabelled Walking Sequences: A Benchmark" is available now. And the code will be released as soon as possible.
• [Mar 2022] More results on GREW are supported, and the model files are coming soon.
• [Mar 2022] Dataset GREW is supported in datasets/GREW.
• [Mar 2022] HID support is ready in datasets/HID.

Highlighted features

• Mutiple Dataset supported: OpenGait supports four popular gait datasets: CASIA-B, OUMVLP, HID, and GREW.
• 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) 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 and models are available in the model zoo.

Authors:

Open Gait Team (OGT)

• Chao Fan (樊超), [email protected]
• Chuanfu Shen (沈川福), [email protected]
• Junhao Liang (梁峻豪), [email protected]

Acknowledgement

• GLN: Saihui Hou (侯赛辉)
• GaitGL: Beibei Lin (林贝贝)
• GREW: GREW TEAM

Note: This code is only used for academic purposes, people cannot use this code for anything that might be considered commercial use.