{"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/vision-based-dynamic-offside-line-marker-for","title":"Vision Based Dynamic Offside Line Marker for Soccer Games","arxiv_id":"1804.06438","date":"2018-04-17","proceeding":null,"authors":["Karthik Muthuraman","Pranav Joshi","Suraj Kiran Raman"],"abstract":"Offside detection in soccer has emerged as one of the most important\ndecisions with an average of 50 offside decisions every game. False detections\nand rash calls adversely affect game conditions and in many cases drastically\nchange the outcome of the game. The human eye has finite precision and can only\ndiscern a limited amount of detail in a given instance. Current offside\ndecisions are made manually by sideline referees and tend to remain\ncontroversial in many games. This calls for automated offside detection\ntechniques in order to assist accurate refereeing. In this work, we have\nexplicitly used computer vision and image processing techniques like Hough\ntransform, color similarity (quantization), graph connected components, and\nvanishing point ideas to identify the probable offside regions.\n  Keywords: Hough transform, connected components, KLT tracking, color\nsimilarity.","url_abs":"http://arxiv.org/abs/1804.06438v1","url_pdf":"http://arxiv.org/pdf/1804.06438v1.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":"vision-based-dynamic-offside-line-marker-for","repo_url":"https://github.com/surajkra/Offside_Tracker_EECS504","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}