{"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/exploring-learning-based-motion-models-in","title":"MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking","arxiv_id":"2403.10826","date":"2024-03-16","proceeding":null,"authors":["Hsiang-Wei Huang","Cheng-Yen Yang","Wenhao Chai","Zhongyu Jiang","Jenq-Neng Hwang"],"abstract":"In the field of multi-object tracking (MOT), traditional methods often rely on the Kalman filter for motion prediction, leveraging its strengths in linear motion scenarios. However, the inherent limitations of these methods become evident when confronted with complex, nonlinear motions and occlusions prevalent in dynamic environments like sports and dance. This paper explores the possibilities of replacing the Kalman filter with a learning-based motion model that effectively enhances tracking accuracy and adaptability beyond the constraints of Kalman filter-based tracker. In this paper, our proposed method MambaMOT and MambaMOT+, demonstrate advanced performance on challenging MOT datasets such as DanceTrack and SportsMOT, showcasing their ability to handle intricate, non-linear motion patterns and frequent occlusions more effectively than traditional methods.","url_abs":"https://arxiv.org/abs/2403.10826v2","url_pdf":"https://arxiv.org/pdf/2403.10826v2.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":"multiple-object-tracking","task_name":"Multiple Object Tracking"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-dancetrack","task":"Multi-Object Tracking","dataset":"DanceTrack","model":"MambaMOT","rank_in_archive_order":26,"of":37,"metrics":{"AssA":"39.0","DetA":"80.8","HOTA":"56.1","IDF1":"54.9","MOTA":"90.3"},"uses_additional_data":true},{"leaderboard":"/sota/multi-object-tracking-on-sportsmot","task":"Multi-Object Tracking","dataset":"SportsMOT","model":"MambaMOT","rank_in_archive_order":13,"of":22,"metrics":{"AssA":"58.6","DetA":"86.7","HOTA":"71.3","IDF1":"71.1","MOTA":"94.9"},"uses_additional_data":true},{"leaderboard":"/sota/multiple-object-tracking-on-sportsmot","task":"Multiple Object Tracking","dataset":"SportsMOT","model":"MambaMOT","rank_in_archive_order":12,"of":19,"metrics":{"AssA":"58.6","DetA":"86.7","HOTA":"71.3","IDF1":"71.1","MOTA":"94.9"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}