CASIA-E: A Large Comprehensive Dataset for Gait Recognition

Gait recognition plays a special role in visual surveillance due to its unique advantage, e.g., long-distance, cross-view and non-cooperative recognition. However, it has not yet been widely applied. One reason for this awkwardness is the lack of a truly big dataset captured in practical outdoor scenarios. Here, the “big” at least means: (1) huge amount of gait videos, (2) sufficient subjects, (3) rich attributes, and (4) spatial and temporal variations. Moreover, most existing large-scale gait datasets are collected indoors, which have few challenges from real scenes, such as the dynamic and complex background clutters, illumination variations, vertical view variations, etc. In this paper, we introduce a newly built big outdoor gait dataset, called CASIA-E. It contains more than one thousand people distributed over near one million videos. Each person involves 26 view angles and varied appearances caused by changes of bag carrying, dressing and walking styles. The videos are captured across five months and across three kinds of outdoor scenes. Soft biometric features are also recorded for all subjects including age, gender, height, weight and nationality.[News!] Now, our dataset is supported by the popular gait recognition project OpenGait. We highly recommend that you can adopt the new setting in (https://github.com/ShiqiYu/OpenGait/blob/master/datasets/CASIA-E/README.md) as the default setting.If you use our dataset in your research, please cite the corresponding paper:Chunfeng Song1, Yongzhen Huang1, Weining Wang, and Liang Wang*, "CASIA-E: A Large Comprehensive Dataset for Gait Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 45, no. 3, pp. 2801-2815, 1 March 2023, doi: 10.1109/TPAMI.2022.3183288. (The first two authors contribute equally to this work)BibTex:@article{casiae2022,title={CASIA-E: a large comprehensive dataset for gait recognition},author={Song, Chunfeng and Huang, Yongzhen and Wang, Weining and Wang, Liang},journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},year={2022},volume={45},number={3},pages={2801-2815},publisher={IEEE}}