{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatial-transformer-network-on-skeleton-based","title":"Spatial Transformer Network on Skeleton-based Gait Recognition","arxiv_id":"2204.03873","date":"2022-04-08","proceeding":null,"authors":["Cun Zhang","Xing-Peng Chen","Guo-Qiang Han","Xiang-Jie Liu"],"abstract":"Skeleton-based gait recognition models usually suffer from the robustness problem, as the Rank-1 accuracy varies from 90\\% in normal walking cases to 70\\% in walking with coats cases. In this work, we propose a state-of-the-art robust skeleton-based gait recognition model called Gait-TR, which is based on the combination of spatial transformer frameworks and temporal convolutional networks. Gait-TR achieves substantial improvements over other skeleton-based gait models with higher accuracy and better robustness on the well-known gait dataset CASIA-B. Particularly in walking with coats cases, Gait-TR get a 90\\% Rank-1 gait recognition accuracy rate, which is higher than the best result of silhouette-based models, which usually have higher accuracy than the silhouette-based gait recognition models. Moreover, our experiment on CASIA-B shows that the spatial transformer can extract gait features from the human skeleton better than the widely used graph convolutional network.","url_abs":"https://arxiv.org/abs/2204.03873v1","url_pdf":"https://arxiv.org/pdf/2204.03873v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatial-transformer-network-on-skeleton-based","repo_url":"https://github.com/bnu-ivc/fastposegait","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"gait-recognition","task_name":"Gait Recognition"}],"methods":[{"method_slug":"spatial-transformer","method_name":"Spatial Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.03873","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}