{"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/mctrack-a-unified-3d-multi-object-tracking","title":"MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving","arxiv_id":"2409.16149","date":"2024-09-23","proceeding":null,"authors":["Xiyang Wang","Shouzheng Qi","Jieyou Zhao","Hangning Zhou","Siyu Zhang","Guoan Wang","Kai Tu","Songlin Guo","Jianbo Zhao","Jian Li","Mu Yang"],"abstract":"This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which often perform well on specific datasets but lack generalizability, MCTrack offers a unified solution. Additionally, we have standardized the format of perceptual results across various datasets, termed BaseVersion, facilitating researchers in the field of multi-object tracking (MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we propose a novel set that assesses motion information output, such as velocity and acceleration, crucial for downstream tasks. The source codes of the proposed method are available at this link: https://github.com/megvii-research/MCTrack}{https://github.com/megvii-research/MCTrack","url_abs":"https://arxiv.org/abs/2409.16149v2","url_pdf":"https://arxiv.org/pdf/2409.16149v2.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":[{"paper_slug":"mctrack-a-unified-3d-multi-object-tracking","repo_url":"https://github.com/megvii-research/mctrack","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-multi-object-tracking","task_name":"3D Multi-Object Tracking"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"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"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-multi-object-tracking-on-waymo-open","task":"3D Multi-Object Tracking","dataset":"Waymo Open Dataset","model":"MCTrack","rank_in_archive_order":2,"of":3,"metrics":{"MOTA/L2":"0.7344"},"uses_additional_data":false},{"leaderboard":"/sota/3d-multi-object-tracking-on-nuscenes","task":"3D Multi-Object Tracking","dataset":"nuScenes","model":"MCTrack","rank_in_archive_order":1,"of":115,"metrics":{"AMOTA":"0.763"},"uses_additional_data":false},{"leaderboard":"/sota/multiple-object-tracking-on-kitti-test","task":"Multiple Object Tracking","dataset":"KITTI Test (Offline Methods)","model":"MCTrack","rank_in_archive_order":1,"of":2,"metrics":{"HOTA":"82.75"},"uses_additional_data":false},{"leaderboard":"/sota/multiple-object-tracking-on-kitti-test-online","task":"Multiple Object Tracking","dataset":"KITTI Test (Online Methods)","model":"MCTrack","rank_in_archive_order":3,"of":34,"metrics":{"HOTA":"81.07","IDSW":"64","MOTA":"89.82"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}