{"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/staple-complementary-learners-for-real-time","title":"Staple: Complementary Learners for Real-Time Tracking","arxiv_id":"1512.01355","date":"2015-12-04","proceeding":"CVPR 2016 6","authors":["Luca Bertinetto","Jack Valmadre","Stuart Golodetz","Ondrej Miksik","Philip Torr"],"abstract":"Correlation Filter-based trackers have recently achieved excellent\nperformance, showing great robustness to challenging situations exhibiting\nmotion blur and illumination changes. However, since the model that they learn\ndepends strongly on the spatial layout of the tracked object, they are\nnotoriously sensitive to deformation. Models based on colour statistics have\ncomplementary traits: they cope well with variation in shape, but suffer when\nillumination is not consistent throughout a sequence. Moreover, colour\ndistributions alone can be insufficiently discriminative. In this paper, we\nshow that a simple tracker combining complementary cues in a ridge regression\nframework can operate faster than 80 FPS and outperform not only all entries in\nthe popular VOT14 competition, but also recent and far more sophisticated\ntrackers according to multiple benchmarks.","url_abs":"http://arxiv.org/abs/1512.01355v2","url_pdf":"http://arxiv.org/pdf/1512.01355v2.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":"staple-complementary-learners-for-real-time","repo_url":"https://github.com/Superlee506/Correlation_Filtering_Tracking_Lib","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"staple-complementary-learners-for-real-time","repo_url":"https://github.com/fengyang95/pyCFTrackers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"staple-complementary-learners-for-real-time","repo_url":"https://github.com/xuduo35/STAPLE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"video-object-tracking","task_name":"Video Object Tracking"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-object-tracking-on-nv-vot211","task":"Video Object Tracking","dataset":"NT-VOT211","model":"Staple","rank_in_archive_order":31,"of":43,"metrics":{"AUC":"31.29","Precision":"39.12"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-trackingnet","task":"Visual Object Tracking","dataset":"TrackingNet","model":"STAPLE_CA","rank_in_archive_order":37,"of":40,"metrics":{"Accuracy":"53.59","Normalized Precision":"60.84","Precision":"46.72"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1512.01355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1512.01355"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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