{"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/e2-go-motion-motion-augmented-event-stream","title":"E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition","arxiv_id":null,"date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Chiara Plizzari","Mirco Planamente","Gabriele Goletto","Marco Cannici","Emanuele Gusso","Matteo Matteucci","Barbara Caputo"],"abstract":"    Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of \"events\". Due to their sensing mechanism, event cameras have little to no motion blur, a very high temporal resolution and require significantly less power and memory than traditional frame-based cameras. These characteristics make them a perfect fit to several real-world applications such as egocentric action recognition on wearable devices, where fast camera motion and limited power challenge traditional vision sensors. However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications. In this paper, we show that event data is a very valuable modality for egocentric action recognition. To do so, we introduce N-EPIC-Kitchens, the first event-based camera extension of the large-scale EPIC-Kitchens dataset. In this context, we propose two strategies: (i) directly processing event-camera data with traditional video-processing architectures (E^2(GO)) and (ii) using event-data to distill optical flow information E^2(GO)MO). On our proposed benchmark, we show that event data provides a comparable performance to RGB and optical flow, yet without any additional flow computation at deploy time, and an improved performance of up to 4% with respect to RGB only information. The N-EPIC-Kitchens dataset is available at https://github.com/EgocentricVision/N-EPIC-Kitchens.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2022/html/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2022/papers/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.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":"e2-go-motion-motion-augmented-event-stream","repo_url":"https://github.com/egocentricvision/n-epic-kitchens","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"event-based-vision","task_name":"Event-based vision"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}