{"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/a-confidence-aware-matching-strategy-for","title":"A Confidence-Aware Matching Strategy For Generalized Multi-Object Tracking","arxiv_id":null,"date":"2024-09-27","proceeding":"IEEE International Conference on Image Processing (ICIP) 2024 9","authors":["Kyujin Shim","Jubi Hwang","Kangwook Ko","Changick Kim"],"abstract":"Multi-object tracking (MOT), a crucial task in computer vision, has broad applicability, and recently, tracking-by-detection-based trackers, which separate the processes of object detection and association, are showing state-of-the-art performance. However, while techniques like feature enhancement and distance measures have been extensively explored, the matching strategy itself remains an area that requires more in-depth study. As a result, many trackers still require manual adjustment of sensitive hyper-parameters for each tracking scenario, limiting their adaptability and robustness in dynamic environments. To address these limitations, we introduce CMTrack, a new tracker featuring a novel confidence-aware matching strategy comprised of three modules: confidence-aware cascade matching (CCM), confidence-aware metric fusion (CMF), and confidence-aware feature update (CFU). Our matching strategy enables the tracker to be a generalized and practical solution for various tracking scenarios within a unified framework while obviating manual calibration of hyper-parameters. The effectiveness of CMTrack is demonstrated through comprehensive assessments of three prominent MOT datasets: MOT17, MOT20, and DanceTrack. Notably, our CMTrack consistently surpasses existing state-of-the-art trackers, showcasing its superior generalization capabilities. The source codes and models are open at https://github.com/kamkyu94/CMTrack.","url_abs":"https://ieeexplore.ieee.org/document/10647729","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10647729","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":"a-confidence-aware-matching-strategy-for","repo_url":"https://github.com/kamkyu94/CMTrack","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-dancetrack","task":"Multi-Object Tracking","dataset":"DanceTrack","model":"CMTrack","rank_in_archive_order":20,"of":37,"metrics":{"AssA":"46.4","HOTA":"61.8","IDF1":"63.3","MOTA":"92.5"},"uses_additional_data":true},{"leaderboard":"/sota/multi-object-tracking-on-mot17","task":"Multi-Object Tracking","dataset":"MOT17","model":"CMTrack","rank_in_archive_order":6,"of":48,"metrics":{"AssA":"66.1","DetA":"65.1","HOTA":"65.5","IDF1":"81.5","MOTA":"80.7"},"uses_additional_data":true},{"leaderboard":"/sota/multi-object-tracking-on-mot20-1","task":"Multi-Object Tracking","dataset":"MOT20","model":"CMTrack","rank_in_archive_order":5,"of":27,"metrics":{"AssA":"66.7","HOTA":"64.8","IDF1":"79.9","MOTA":"76.2"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}