Welcome to the official repository for the paper "Cross-Covariate Gait Recognition: A Benchmark," which has been accepted to AAAI 2024. In this work, we introduce a novel dataset and parsing-based gait recognition method. Below you'll find details about the paper, dataset, and accompanying resources.
- AAAI 2024 Version: Download paper (AAAI)
- ArXiv Preprint: Download preprint (ArXiv)
Here are some visualizations that depict the characteristics of the Cross-Covariate Gait Recognition (CCGR) dataset in comparison with other existing datasets, as well as the distribution of covariates, viewpoints, and data modalities within CCGR.
We are pleased to offer the derived data for use in your research projects. To ensure proper usage, we kindly ask that you adhere to the following terms:
- Research Purposes Only: The dataset is strictly for academic and non-commercial research purposes only.
- No Modification or Redistribution: Modifying or distributing the dataset in
any form
is not permitted. - Internal Use: The dataset is to be used within your organization or research group and is not to be shared outside of this group.
By downloading the dataset, you indicate your acceptance of these terms. We have facilitated easy access to the data via the links provided below, so no additional download request is necessary!
You are invited to download the CCGR dataset from the links below. Before proceeding with the download, please ensure you have agreed to the above terms.
- Baidu Netdisk: Download Dataset (Code:ngcw)
- OneDrive: Download Dataset
We wish to support a collaborative and respectful research community and we believe that adherence to these terms will be mutually beneficial.
I am very sorry. I have been too busy lately, and the data is large. Please understand! If you want to use RGB data immediately, please send me an email to contact me.
We would like to inform you that the Raw data is scheduled to be released on March 1, 2024, because we need more time to upload the data (the raw data is bigger). Details regarding access and download instructions will be announced as the date approaches. Please stay tuned to our official communication channels for further updates.
Thank you for your attention and cooperation. We sincerely hope the dataset aids you in your research endeavors.
All subjects are openly recruited and participate voluntarily, and they need to read and acknowledge the collection protocol by signature and fingerprint. In return, we pay each subject a fee for data copyright.
Specific adjustments are necessary to utilize the OpenGait framework testing the CCGR dataset effectively. For detailed instructions on how to adapt OpenGait, please refer to the guidelines provided at the beginning of the CCGR_EVA.py
file. These modifications are designed to ensure seamless integration of the CCGR dataset with the existing capabilities of OpenGait. Additionally, we have included some yaml files within this document and some trained weights for silhouette. Baidu Netdisk (Code:4o92); OneDrive.
If you find our dataset or paper useful for your research, please consider citing:
@inproceedings{Zou2024CCGR,
title={Cross-Covariate Gait Recognition: A Benchmark},
author={Zou, Shinan and Fan, Chao and Xiong, Jianbo and Shen, Chuanfu and Yu, Shiqi and Tang, Jin},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2024}
}