{"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/compressive-online-robust-principal-component","title":"Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation","arxiv_id":"1710.09160","date":"2017-10-25","proceeding":null,"authors":["Srivatsa Prativadibhayankaram","Huynh Van Luong","Thanh-Ha Le","Andre Kaup"],"abstract":"In the context of online Robust Principle Component Analysis (RPCA) for the\nvideo foreground-background separation, we propose a compressive online RPCA\nwith optical flow that separates recursively a sequence of frames into sparse\n(foreground) and low-rank (background) components. Our method considers a small\nset of measurements taken per data vector (frame), which is different from\nconventional batch RPCA, processing all the data directly. The proposed method\nalso incorporates multiple prior information, namely previous foreground and\nbackground frames, to improve the separation and then updates the prior\ninformation for the next frame. Moreover, the foreground prior frames are\nimproved by estimating motions between the previous foreground frames using\noptical flow and compensating the motions to achieve higher quality foreground\nprior. The proposed method is applied to online video foreground and background\nseparation from compressive measurements. The visual and quantitative results\nshow that our method outperforms the existing methods.","url_abs":"http://arxiv.org/abs/1710.09160v1","url_pdf":"http://arxiv.org/pdf/1710.09160v1.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":"compressive-online-robust-principal-component","repo_url":"https://github.com/huynhlvd/corpca-of","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}