{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:25Z","timestamp":1761176125903,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Multiple Object Tracking (MOT) aims to detect all objects in the scene and associate them across frames with unique ID. Within tracking-by-detection (TBD) paradigm, the confidence based two-stage matching scheme has become popular in MOT. However, when two detections are matched to the same trajectory, the one with higher confidence score usually takes precedence over the lower one, even if the lower one is the ground-truth, causing ID switches (IDS). Considering this, we propose a tailored filtering mechanism to handle the low-confident detections in a more reasonable way. Besides, we introduce a novel fusion scheme for appearance and motion information based on appearance clarity and localization accuracy of the detection boxes. Finally, an adaptive management of unmatched detections scheme is proposed to reduce the occurrence of IDS and duplicate trajectories. Extensive experiments have been conducted on MOT17 and MOT20, in which our tracker exhibits stronger identity preservation capabilities against other competitors.<\/jats:p>","DOI":"10.3233\/faia250837","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:36Z","timestamp":1761126216000},"source":"Crossref","is-referenced-by-count":0,"title":["DetTrack: Realizing Strong Identity Preservation in Multi-Object Tracking via exploration of Detection Information"],"prefix":"10.3233","author":[{"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Sichuan University, China"}]},{"given":"Yi","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sichuan University, China"}]},{"given":"Chen","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sichuan University, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250837","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:36Z","timestamp":1761126216000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250837","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}