{"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/distribution-of-action-movements-dam-a-1","title":"Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition","arxiv_id":"2310.17421","date":"2023-10-26","proceeding":"Frontiers of Computer Science 2015 9","authors":["Facundo Manuel Quiroga","Franco Ronchetti","Laura Lanzarini","Cesar Eestrebou"],"abstract":"Human action recognition from skeletal data is an important and active area of research in which the state of the art has not yet achieved near-perfect accuracy on many well-known datasets. In this paper, we introduce the Distribution of Action Movements Descriptor, a novel action descriptor based on the distribution of the directions of the motions of the joints between frames, over the set of all possible motions in the dataset. The descriptor is computed as a normalized histogram over a set of representative directions of the joints, which are in turn obtained via clustering. While the descriptor is global in the sense that it represents the overall distribution of movement directions of an action, it is able to partially retain its temporal structure by applying a windowing scheme. The descriptor, together with a standard classifier, outperforms several state-of-the-art techniques on many well-known datasets.","url_abs":"https://arxiv.org/abs/2310.17421v1","url_pdf":"https://arxiv.org/pdf/2310.17421v1.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":[],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/skeleton-based-action-recognition-on-msr","task":"Skeleton Based Action Recognition","dataset":"MSR Action3D","model":"Distribution of Action Movements + SVM","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"94%"},"uses_additional_data":false},{"leaderboard":"/sota/skeleton-based-action-recognition-on-msrc-12","task":"Skeleton Based Action Recognition","dataset":"MSRC-12","model":"Distribution of Action Movements + SVM","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"91.7%"},"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}