{"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/integrating-boundary-and-center-correlation","title":"Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation","arxiv_id":"1710.02039","date":"2017-10-05","proceeding":null,"authors":["Feng Li","Yingjie Yao","Peihua Li","David Zhang","WangMeng Zuo","Ming-Hsuan Yang"],"abstract":"The aspect ratio variation frequently appears in visual tracking and has a\nsevere influence on performance. Although many correlation filter (CF)-based\ntrackers have also been suggested for scale adaptive tracking, few studies have\nbeen given to handle the aspect ratio variation for CF trackers. In this paper,\nwe make the first attempt to address this issue by introducing a family of 1D\nboundary CFs to localize the left, right, top, and bottom boundaries in videos.\nThis allows us cope with the aspect ratio variation flexibly during tracking.\nSpecifically, we present a novel tracking model to integrate 1D Boundary and 2D\nCenter CFs (IBCCF) where boundary and center filters are enforced by a\nnear-orthogonality regularization term. To optimize our IBCCF model, we develop\nan alternating direction method of multipliers. Experiments on several datasets\nshow that IBCCF can effectively handle aspect ratio variation, and achieves\nstate-of-the-art performance in terms of accuracy and robustness.","url_abs":"http://arxiv.org/abs/1710.02039v1","url_pdf":"http://arxiv.org/pdf/1710.02039v1.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":"integrating-boundary-and-center-correlation","repo_url":"https://github.com/lifeng9472/IBCCF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"visual-tracking","task_name":"Visual Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.02039","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}