{"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/a-universal-update-pacing-framework-for","title":"A Universal Update-pacing Framework For Visual Tracking","arxiv_id":"1603.00132","date":"2016-03-01","proceeding":null,"authors":["Zexi Hu","Yuefang Gao","Dong Wang","Xuhong Tian"],"abstract":"This paper proposes a novel framework to alleviate the model drift problem in\nvisual tracking, which is based on paced updates and trajectory selection.\nGiven a base tracker, an ensemble of trackers is generated, in which each\ntracker's update behavior will be paced and then traces the target object\nforward and backward to generate a pair of trajectories in an interval. Then,\nwe implicitly perform self-examination based on trajectory pair of each tracker\nand select the most robust tracker. The proposed framework can effectively\nleverage temporal context of sequential frames and avoid to learn corrupted\ninformation. Extensive experiments on the standard benchmark suggest that the\nproposed framework achieves superior performance against state-of-the-art\ntrackers.","url_abs":"http://arxiv.org/abs/1603.00132v1","url_pdf":"http://arxiv.org/pdf/1603.00132v1.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":"a-universal-update-pacing-framework-for","repo_url":"https://github.com/huzexi/huzexi","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"visual-tracking","task_name":"Visual Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}