{"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/overlap-suppression-clustering-for-offline","title":"Overlap Suppression Clustering for Offline Multi-Camera People Tracking","arxiv_id":null,"date":"2024-06-17","proceeding":"CVPR 2024 6","authors":["Ryuto Yoshida","Junichi Okubo","Junichiro Fujii","Masazumi Amakata","Takayoshi Yamashita"],"abstract":"Multi-Camera People Tracking is a multifaceted issue that requires the integration of several computer vision tasks such as Object Detection Multiple Object Tracking and Person Re-identification. This study presents a multi-camera people tracking method that comprises four main processes: (1) single camera people tracking based on overlap suppression clustering (2) representative image extraction using pose estimation for re-identification (3) re-identification using hierarchical clustering with average linkage and (4) low-identifiability tracklets assignment. Our RIIPS team achieved the highest Higher Order Tracking Accuracy (HOTA) of 71.9446% in the 2024 AI City Challenge Track 1.","url_abs":"https://openaccess.thecvf.com/content/CVPR2024W/AICity/html/Yoshida_Overlap_Suppression_Clustering__for_Offline_Multi-Camera_People_Tracking_CVPRW_2024_paper.html","url_pdf":"https://openaccess.thecvf.com/content/CVPR2024W/AICity/papers/Yoshida_Overlap_Suppression_Clustering__for_Offline_Multi-Camera_People_Tracking_CVPRW_2024_paper.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":[],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"multiple-object-tracking","task_name":"Multiple Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-2024-ai-city","task":"Multi-Object Tracking","dataset":"2024 AI City Challenge","model":"Yachiyo","rank_in_archive_order":2,"of":8,"metrics":{"AssA":"71.81","DetA":"72.10","HOTA":"71.94","LocA":"88.39"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}