{"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/online-multi-camera-people-tracking-with","title":"Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering","arxiv_id":null,"date":"2024-06-17","proceeding":"CVPR 2024 6","authors":["Riu Cherdchusakulchai","Sasin Phimsiri","Visarut Trairattanapa","Suchat Tungjitnob","Wasu Kudisthalert","Pornprom Kiawjak","Ek Thamwiwatthana","Phawat Borisuitsawat","Teepakorn Tosawadi","Pakcheera Choppradit","Kasisdis Mahakijdechachai","Supawit Vatathanavaro","Worawit Saetan","Vasin Suttichaya"],"abstract":"Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multi-object Tracking (MOT) Multi-target Multi-camera Tracking (MTMC) and Cross Interval Synchronization (CIS). In the MOT stage ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556% ranking sixth. The code is available at: https://github.com/AI-and-Robotics-Ventures/AIC2024_Track1_ARV","url_abs":"https://openaccess.thecvf.com/content/CVPR2024W/AICity/html/Cherdchusakulchai_Online_Multi-camera_People_Tracking_with_Spatial-temporal_Mechanism_and_Anchor-feature_Hierarchical_CVPRW_2024_paper.html","url_pdf":"https://openaccess.thecvf.com/content/CVPR2024W/AICity/papers/Cherdchusakulchai_Online_Multi-camera_People_Tracking_with_Spatial-temporal_Mechanism_and_Anchor-feature_Hierarchical_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":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"}],"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":"ARV","rank_in_archive_order":7,"of":8,"metrics":{"AssA":"48.07","DetA":"54.85","HOTA":"51.06","LocA":"89.61"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}