{"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/activenet-a-computer-vision-based-approach-to","title":"ActiveNet: A computer-vision based approach to determine lethargy","arxiv_id":"2010.13714","date":"2020-10-26","proceeding":null,"authors":["Aitik Gupta","Aadit Agarwal"],"abstract":"The outbreak of COVID-19 has forced everyone to stay indoors, fabricating a significant drop in physical activeness. Our work is constructed upon the idea to formulate a backbone mechanism, to detect levels of activeness in real-time, using a single monocular image of a target person. The scope can be generalized under many applications, be it in an interview, online classes, security surveillance, et cetera. We propose a Computer Vision based multi-stage approach, wherein the pose of a person is first detected, encoded with a novel approach, and then assessed by a classical machine learning algorithm to determine the level of activeness. An alerting system is wrapped around the approach to provide a solution to inhibit lethargy by sending notification alerts to individuals involved.","url_abs":"https://arxiv.org/abs/2010.13714v1","url_pdf":"https://arxiv.org/pdf/2010.13714v1.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":"activenet-a-computer-vision-based-approach-to","repo_url":"https://github.com/aaditagarwal/ActiveNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"activeness-detection","task_name":"Activeness Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/activeness-detection-on-coco-test-dev","task":"Activeness Detection","dataset":"COCO test-dev","model":"Lightweight OpenPose","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (%)":"76.67"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}