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Focus on this problem, we propose a gait recognition network, Multi-scale Spatio-Temporal Gait (MST-Gait), which can learn multi-scale gait information simultaneously from spatial and temporal dimensions. We design a multi-scale spatio-temporal groups Transformer (MSTGT) to model the correlation of intra-frame and inter-frame joints simultaneously. And a multi-scale segmentation strategy is designed to capture the periodic and local features of the gait. To fully exploit the temporal information of gait motion, we design a fusion temporal convolution (FTC) to aggregate temporal information at different scales and motion information. Experiments on the popular CASIA-B gait dataset and OUMVLP-Pose dataset show that our method outperforms most existing skeleton-based methods, verifying the effectiveness of the proposed modules.<\/jats:p>","DOI":"10.3233\/aic-230033","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T11:41:31Z","timestamp":1696333291000},"page":"297-310","source":"Crossref","is-referenced-by-count":2,"title":["Multi-scale spatio-temporal network for skeleton-based gait recognition"],"prefix":"10.1177","volume":"36","author":[{"given":"Dongzhi","family":"He","sequence":"first","affiliation":[{"name":"College of Software Engineering, Beijing University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongle","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Beijing University of Technology, Beijing, 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