{"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/cvabs-moving-object-segmentation-with-common","title":"CVABS: Moving Object Segmentation with Common Vector Approach for Videos","arxiv_id":"1810.08412","date":"2018-10-19","proceeding":null,"authors":["Şahin Işık","Kemal Özkan","Ömer Nezih Gerek"],"abstract":"Background modelling is a fundamental step for several real-time computer\nvision applications that requires security systems and monitoring. An accurate\nbackground model helps detecting activity of moving objects in the video. In\nthis work, we have developed a new subspace based background modelling\nalgorithm using the concept of Common Vector Approach with Gram-Schmidt\northogonalization. Once the background model that involves the common\ncharacteristic of different views corresponding to the same scene is acquired,\na smart foreground detection and background updating procedure is applied based\non dynamic control parameters. A variety of experiments is conducted on\ndifferent problem types related to dynamic backgrounds. Several types of\nmetrics are utilized as objective measures and the obtained visual results are\njudged subjectively. It was observed that the proposed method stands\nsuccessfully for all problem types reported on CDNet2014 dataset by updating\nthe background frames with a self-learning feedback mechanism.","url_abs":"http://arxiv.org/abs/1810.08412v1","url_pdf":"http://arxiv.org/pdf/1810.08412v1.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":"cvabs-moving-object-segmentation-with-common","repo_url":"https://github.com/isahhin/cvabs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"self-learning","task_name":"Self-Learning"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}