{"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/a-robust-road-vanishing-point-detection","title":"A Robust Road Vanishing Point Detection Adapted to the Real-World Driving Scenes","arxiv_id":null,"date":"2021-03-18","proceeding":"Sensors 2021 3","authors":["Cuong Nguyen Khac","Yeongyu Choi","Ju H. Park and Ho-Youl Jung"],"abstract":"Vanishing point (VP) provides extremely useful information related to roads in driving\r\nscenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Existing VP\r\ndetection methods for driving scenes still have not achieved sufficiently high accuracy and robustness\r\nto apply for real-world driving scenes. This paper proposes a robust motion-based road VP detection\r\nmethod to compensate for the deficiencies. For such purposes, three main processing steps often\r\nused in the existing road VP detection methods are carefully examined. Based on the analysis,\r\nstable motion detection, stationary point-based motion vector selection, and angle-based RANSAC\r\n(RANdom SAmple Consensus) voting are proposed. A ground-truth driving dataset including\r\nvarious objects and illuminations is used to verify the robustness and real-time capability of the\r\nproposed method. The experimental results show that the proposed method outperforms the existing\r\nmotion-based and edge-based road VP detection methods for various illumination conditioned\r\ndriving scenes.","url_abs":"https://www.mdpi.com/1424-8220/21/6/2133","url_pdf":"https://www.mdpi.com/1424-8220/21/6/2133/pdf?version=1616215741","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":"a-robust-road-vanishing-point-detection","repo_url":"https://github.com/DomhnallBoyle/comma-ai-calib-challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"motion-detection","task_name":"Motion Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}