{"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/gaf-net-video-based-person-re-identification","title":"GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions","arxiv_id":null,"date":"2024-02-27","proceeding":"International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2024 2","authors":["Moncef Boujou","Rabah Iguernaissi","Lionel Nicod","Djamal Merad","Séverine Dubuisson"],"abstract":"Video-based person re-identification (Re-ID) is a challenging task aiming to match individuals across various cameras based on video sequences. While most existing Re-ID techniques focus solely on appearance information, including gait information, could potentially improve person Re-ID systems. In this study, we propose, GAF-Net, a novel approach that integrates appearance with gait features for re-identifying individuals; the appearance features are extracted from RGB tracklets while the gait features are extracted from skeletal pose estimation. These features are then combined into a single feature allowing the re-identification of individuals. Our numerical experiments on the iLIDS-Vid dataset demonstrate the efficacy of skeletal gait features in enhancing the performance of person Re-ID systems. Moreover, by incorporating the state-of-the-art PiT network within the GAF-Net framework, we improve both rank-1 and rank-5 accuracy by 1 percentage point.","url_abs":"https://www.scitepress.org/Link.aspx?doi=10.5220/0012364200003660","url_pdf":"https://www.researchgate.net/publication/378702584_GAF-Net_Video-Based_Person_Re-Identification_via_Appearance_and_Gait_Recognitions","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":"gaf-net-video-based-person-re-identification","repo_url":"https://github.com/Moncef-Bj/GAF-Net-for-Video-Based-Person-Re-Identification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"gaf-net-video-based-person-re-identification","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/6/GAF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"video-based-person-re-identification","task_name":"Video-Based Person Re-Identification"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-ilids-vid","task":"Person Re-Identification","dataset":"iLIDS-VID","model":"GAF-Net","rank_in_archive_order":1,"of":10,"metrics":{"Rank-1":"93.07","Rank-10":"99.74","Rank-20":"99.94","Rank-5":"99.27"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}