{"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/learning-background-aware-correlation-filters","title":"Learning Background-Aware Correlation Filters for Visual Tracking","arxiv_id":"1703.04590","date":"2017-03-14","proceeding":"ICCV 2017 10","authors":["Hamed Kiani Galoogahi","Ashton Fagg","Simon Lucey"],"abstract":"Correlation Filters (CFs) have recently demonstrated excellent performance in\nterms of rapidly tracking objects under challenging photometric and geometric\nvariations. The strength of the approach comes from its ability to efficiently\nlearn - \"on the fly\" - how the object is changing over time. A fundamental\ndrawback to CFs, however, is that the background of the object is not be\nmodelled over time which can result in suboptimal results. In this paper we\npropose a Background-Aware CF that can model how both the foreground and\nbackground of the object varies over time. Our approach, like conventional CFs,\nis extremely computationally efficient - and extensive experiments over\nmultiple tracking benchmarks demonstrate the superior accuracy and real-time\nperformance of our method compared to the state-of-the-art trackers including\nthose based on a deep learning paradigm.","url_abs":"http://arxiv.org/abs/1703.04590v2","url_pdf":"http://arxiv.org/pdf/1703.04590v2.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":"learning-background-aware-correlation-filters","repo_url":"https://github.com/4kubo/bacf_python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"video-object-tracking","task_name":"Video Object Tracking"},{"task_slug":"visual-tracking","task_name":"Visual Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-object-tracking-on-nv-vot211","task":"Video Object Tracking","dataset":"NT-VOT211","model":"BACF","rank_in_archive_order":37,"of":43,"metrics":{"AUC":"26.29","Precision":"35.05"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.04590","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}