Keskeiset käsitteet
The author highlights the challenges of cross-covariate gait recognition and introduces the CCGR dataset to address these challenges. The proposed ParsingGait framework shows promising results for advancing gait recognition.
Tiivistelmä
The content discusses the creation of the Cross-Covariate Gait Recognition (CCGR) dataset, emphasizing its population and individual-level diversity. It introduces ParsingGait as a novel approach to address cross-covariate challenges in gait recognition. The analysis includes evaluations of different covariates, views, and the impact of parsing on recognition accuracy.
The CCGR dataset comprises 970 subjects with 1.6 million sequences, offering diverse walking conditions and filming views. ParsingGait demonstrates significant potential for improving gait recognition accuracy. Covariates like carrying items, road types, speed, clothing, and walking styles impact recognition performance.
The study evaluates single-covariate and mixed-covariate scenarios using both "easy" and "hard" metrics to assess their influence on gait recognition accuracy. Results show that individual-level diversity poses significant challenges in gait recognition tasks.
Tilastot
CCGR dataset has 970 subjects with about 1.6 million sequences.
Existing SOTA methods achieve less than 43% accuracy on CCGR.
ParsingGait demonstrates remarkable potential for further advancement.
Lainaukset
"The CCGR dataset provides comprehensive resources for exploring cross-covariate gait recognition."
"Parsing-based gait recognition shows promising results for addressing complex covariate challenges."