{"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/moving-object-detection-for-event-based-2","title":"Moving Object Detection for Event-based vision using Graph Spectral Clustering","arxiv_id":"2109.14979","date":"2021-09-30","proceeding":"International Conference on Computer Vision Workshops 2021 10","authors":["Anindya Mondal","Shashant R","Jhony H. Giraldo","Thierry Bouwmans","Ananda S. Chowdhury"],"abstract":"Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-based cameras, these sensors capture a stream of asynchronous 'events' that pose multiple advantages over the former, like high dynamic range, low latency, low power consumption, and reduced motion blur. However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution. Moreover, as event-based cameras can only capture the relative changes in brightness of a scene, event data do not contain usual visual information (like texture and color) as available in video data from normal cameras. So, moving object detection in event-based cameras becomes an extremely challenging task. In this paper, we present an unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data (GSCEventMOD). We additionally show how the optimum number of moving objects can be automatically determined. Experimental comparisons on publicly available datasets show that the proposed GSCEventMOD algorithm outperforms a number of state-of-the-art techniques by a maximum margin of 30%.","url_abs":"https://arxiv.org/abs/2109.14979v3","url_pdf":"https://arxiv.org/pdf/2109.14979v3.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":"moving-object-detection-for-event-based-2","repo_url":"https://github.com/anindya2001/GSCEventMOD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"curved-text-detection","task_name":"Curved Text Detection"},{"task_slug":"event-based-vision","task_name":"Event-based vision"},{"task_slug":"moving-object-detection","task_name":"Moving Object Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"spectral-clustering","method_name":"Spectral Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/moving-object-detection-on-dvsmotion20","task":"Moving Object Detection","dataset":"DVSMOTION20","model":"GSCEventMOD","rank_in_archive_order":1,"of":1,"metrics":{"F-Measure":"66.93"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.14979","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}