{"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-object-tracking-challenge-technical","title":"Multiple Object Tracking Challenge Technical Report for Team MT_IoT","arxiv_id":"2212.03586","date":"2022-12-07","proceeding":null,"authors":["Feng Yan","Zhiheng Li","Weixin Luo","Zequn Jie","Fan Liang","Xiaolin Wei","Lin Ma"],"abstract":"This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.","url_abs":"https://arxiv.org/abs/2212.03586v1","url_pdf":"https://arxiv.org/pdf/2212.03586v1.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":"multiple-object-tracking-challenge-technical","repo_url":"https://github.com/BingfengYan/DS_OCSORT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"human-detection","task_name":"Human Detection"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"word-sense-disambiguation","task_name":"Word Sense Disambiguation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"cspdarknet53","method_name":"CSPDarknet53"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"yolox","method_name":"YOLOX"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-dancetrack","task":"Multi-Object Tracking","dataset":"DanceTrack","model":"MT_IOT","rank_in_archive_order":10,"of":37,"metrics":{"AssA":"52.95","DetA":"84.14","HOTA":"66.66","IDF1":"70.6","MOTA":"93.97"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2212.03586","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}