{"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/human-activity-recognition-a-spatio-temporal","title":"Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection","arxiv_id":null,"date":"2020-02-08","proceeding":"International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2020 2","authors":["Nassim Mokhtari","Alexis Nédélec","Pierre De Loor"],"abstract":"Human activity recognition (HAR) based on skeleton data that can be extracted from videos (Kinect for example) , or provided by a depth camera is a time series classification problem, where handling both spatial and temporal dependencies is a crucial task, in order to achieve a good recognition. In the online human activity recognition, identifying the beginning and end of an action is an important element, that might be difficult in a continuous data flow. In this work, we present a 3D skeleton data encoding method to generate an image that preserves the spatial and temporal dependencies existing between the skeletal joints.To allow online action detection we combine this encoding system with a sliding window on the continous data stream. By this way, no start or stop timestamp is needed and the recognition can be done at any moment. A deep learning CNN algorithm is used to achieve actions online detection.","url_abs":"https://www.scitepress.org/PublicationsDetail.aspx?ID=rsbxRj6Ic2Y=&t=1","url_pdf":"https://www.scitepress.org/PublicationsDetail.aspx?ID=rsbxRj6Ic2Y=&t=1","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":"human-activity-recognition-a-spatio-temporal","repo_url":"https://github.com/nassimmokhtari/Spatio-Temporal-Image-Encoding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"},{"task_slug":"online-action-detection","task_name":"Online Action Detection"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[{"method_slug":"vgg-16","method_name":"VGG-16"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/human-activity-recognition-on-oad-dataset","task":"Human Activity Recognition","dataset":"OAD dataset","model":"STIE + VGG16(fine-tuning)","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"86.81"},"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}