{"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/finesports-a-multi-person-hierarchical-sports","title":"FineSports: A Multi-person Hierarchical Sports Video Dataset for Fine-grained Action Understanding","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Jinglin Xu","Guohao Zhao","Sibo Yin","Wenhao Zhou","Yuxin Peng"],"abstract":"    Fine-grained action analysis in multi-person sports is complex due to athletes' quick movements and intense physical confrontations which result in severe visual obstructions in most scenes. In addition accessible multi-person sports video datasets lack fine-grained action annotations in both space and time adding to the difficulty in fine-grained action analysis. To this end we construct a new multi-person basketball sports video dataset named FineSports which contains fine-grained semantic and spatial-temporal annotations on 10000 NBA game videos covering 52 fine-grained action types 16000 action instances and 123000 spatial-temporal bounding boxes. We also propose a new prompt-driven spatial-temporal action location approach called PoSTAL composed of a prompt-driven target action encoder (PTA) and an action tube-specific detector (ATD) to directly generate target action tubes with fine-grained action types without any off-line proposal generation. Extensive experiments on the FineSports dataset demonstrate that PoSTAL outperforms state-of-the-art methods. Data and code are available at https://github.com/PKU-ICST-MIPL/FineSports_CVPR2024.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Xu_FineSports_A_Multi-person_Hierarchical_Sports_Video_Dataset_for_Fine-grained_Action_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Xu_FineSports_A_Multi-person_Hierarchical_Sports_Video_Dataset_for_Fine-grained_Action_CVPR_2024_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":"finesports-a-multi-person-hierarchical-sports","repo_url":"https://github.com/pku-icst-mipl/finesports_cvpr2024","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-analysis","task_name":"Action Analysis"},{"task_slug":"action-understanding","task_name":"Action Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}