{"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-multi-scale-recurrent-framework-for-motion","title":"A Multi-Scale Recurrent Framework for Motion Segmentation With Event Camera","arxiv_id":null,"date":"2023-07-28","proceeding":"IEEE Access 2023 7","authors":["Shaobo Zhang","Lei Sun","Kaiwei Wang"],"abstract":"Motion segmentation is a formidable computer vision task, aiming to segment moving targets from a dynamic scene. In this paper, we choose to introduce an additional modality to bolster the robustness. The event camera is a bio-inspired sensor that accurately detects and captures intensity changes with exceptional temporal resolution and dynamic range, which is an optimal choice for motion segmentation. Therefore, we present a novel framework for event-based motion segmentation and propose Multi-Scale Recurrent Neural Network (MSRNN) to fuse temporal information efficiently. To our best knowledge, it is the first time that a multi-scale recurrent architecture is implemented in event-based motion segmentation. The proposed framework is evaluated through experiments conducted on the EV-IMO dataset. Our method achieves a mean Intersection-over-Union (mIoU) of 82.0%, which sets a new state-of-the-art in motion segmentation. To further validate our approach in arduous real-world scenarios, we introduce the Event Challenging Motion dataset, consisting of 350 images and corresponding events, in which our method outperforms the other methods by 1.5% in Intersection-over-Union (IoU).","url_abs":"https://ieeexplore.ieee.org/abstract/document/10196434","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10196434","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-multi-scale-recurrent-framework-for-motion","repo_url":"https://github.com/shaobo007/msrnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"motion-segmentation","task_name":"Motion Segmentation"},{"task_slug":"segmentation","task_name":"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}