  Gait datasets are essential for gait research. However, this paper observes
that present benchmarks, whether conventional constrained or emerging
real-world datasets, fall short regarding covariate diversity. To bridge this
gap, we undertake an arduous 20-month effort to collect a cross-covariate gait
recognition (CCGR) dataset. The CCGR dataset has 970 subjects and about 1.6
million sequences; almost every subject has 33 views and 53 different
covariates. Compared to existing datasets, CCGR has both population and
individual-level diversity. In addition, the views and covariates are well
labeled, enabling the analysis of the effects of different factors. CCGR
provides multiple types of gait data, including RGB, parsing, silhouette, and
pose, offering researchers a comprehensive resource for exploration. In order
to delve deeper into addressing cross-covariate gait recognition, we propose
parsing-based gait recognition (ParsingGait) by utilizing the newly proposed
parsing data. We have conducted extensive experiments. Our main results show:
1) Cross-covariate emerges as a pivotal challenge for practical applications of
gait recognition. 2) ParsingGait demonstrates remarkable potential for further
advancement. 3) Alarmingly, existing SOTA methods achieve less than 43%
accuracy on the CCGR, highlighting the urgency of exploring cross-covariate
gait recognition. Link: https://github.com/ShinanZou/CCGR.
