{"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/dynamic-vision-sensors-for-human-activity","title":"Dynamic Vision Sensors for Human Activity Recognition","arxiv_id":"1803.04667","date":"2018-03-13","proceeding":null,"authors":["Stefanie Anna Baby","Bimal Vinod","Chaitanya Chinni","Kaushik Mitra"],"abstract":"Unlike conventional cameras which capture video at a fixed frame rate,\nDynamic Vision Sensors (DVS) record only changes in pixel intensity values. The\noutput of DVS is simply a stream of discrete ON/OFF events based on the\npolarity of change in its pixel values. DVS has many attractive features such\nas low power consumption, high temporal resolution, high dynamic range and\nfewer storage requirements. All these make DVS a very promising camera for\npotential applications in wearable platforms where power consumption is a major\nconcern.\n  In this paper, we explore the feasibility of using DVS for Human Activity\nRecognition (HAR). We propose to use the various slices (such as $x-y$, $x-t$,\nand $y-t$) of the DVS video as a feature map for HAR and denote them as Motion\nMaps. We show that fusing motion maps with Motion Boundary Histogram (MBH) give\ngood performance on the benchmark DVS dataset as well as on a real DVS gesture\ndataset collected by us. Interestingly, the performance of DVS is comparable to\nthat of conventional videos although DVS captures only sparse motion\ninformation.","url_abs":"http://arxiv.org/abs/1803.04667v1","url_pdf":"http://arxiv.org/pdf/1803.04667v1.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":"dynamic-vision-sensors-for-human-activity","repo_url":"https://github.com/Computational-Imaging-Lab-IITM/HAR-DVS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}