Awesome

GREW-BENCHMARCH

This repository contains the code for our ICCV 2021 paper Gait Recognition in the Wild: A Benchmark

Getting Started

• Apply for and Download GREW datasets.
• Decompress and Organize GREW datasets.
• Training and Testing on GREW datasets.

Acknowledgements

Part of the code is adopted from previous works: GaitSet, We thank the original authors for their awesome repos.

Besides, some other attractive works extend the boundary of GREW.

• DyGait (ICCV2023)
• SPOSGait

Citing

If you find this code useful, please consider to cite our work.

@inproceedings{zhu2021gait,
    title={Gait Recognition in the Wild: A Benchmark},
    author={Zheng Zhu, Xianda Guo, Tian Yang, Junjie Huang, 
        Jiankang Deng, Guan Huang, Dalong Du,Jiwen Lu, Jie Zhou},
    booktitle={IEEE International Conference on Computer Vision (ICCV)},
    year={2021}              
}
@article{guo2022gait,
  title={Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline},
  author={Guo, Xianda and Zhu, Zheng and Yang, Tian and Lin, Beibei and Huang, Junjie and Deng, Jiankang and Huang, Guan and Zhou, Jie and Lu, Jiwen},
  journal={arXiv e-prints},
  pages={arXiv--2205},
  year={2022}
}

License

The GREW dataset is freely available for non-commercial use and may be redistributed under these conditions. If you have any commercial questions, you can contact Zheng Zhu.