{"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/multiple-people-tracking-using-body-and-joint","title":"Multiple People Tracking using Body and Joint Detections","arxiv_id":null,"date":"2019-06-01","proceeding":"The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019 6","authors":["Roberto Henschel","Yunzhe Zou","Bodo Rosenhahn"],"abstract":"Most multiple people tracking systems compute trajectories based on the tracking-by-detection paradigm. Consequently, the performance depends to a large extent on the quality of the employed input detections. However, despite an enormous progress in recent years, partially occluded people are still often not recognized. Also, many correct detections are mistakenly discarded when the non-maximum suppression is performed. Improving the tracking performance thus requires to augment the coarse input. Wellsuited for this task are fine-graded body joint detections, as they allow to locate even strongly occluded persons.\r\nThus in this work, we analyze the suitability of including joint detections for multiple people tracking. We introduce different affinities between the two detection types and evaluate their performances. Tracking is then performed within a near-online framework based on a min cost graph labeling formulation. As a result, our framework can recover heavily occluded persons and solve the data association efficiently. We evaluate our framework on the MOT16/17 benchmark. Experimental results demonstrate that our framework achieves state-of-the-art results.","url_abs":"http://openaccess.thecvf.com/content_CVPRW_2019/html/BMTT/Henschel_Multiple_People_Tracking_Using_Body_and_Joint_Detections_CVPRW_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPRW_2019/papers/BMTT/Henschel_Multiple_People_Tracking_Using_Body_and_Joint_Detections_CVPRW_2019_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":"multiple-people-tracking","task_name":"Multiple People Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-mot17","task":"Multi-Object Tracking","dataset":"MOT17","model":"JBNOT","rank_in_archive_order":45,"of":48,"metrics":{"MOTA":"52.6"},"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}