{"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/fine-grained-activity-recognition-in-baseball","title":"Fine-grained Activity Recognition in Baseball Videos","arxiv_id":"1804.03247","date":"2018-04-09","proceeding":null,"authors":["AJ Piergiovanni","Michael S. Ryoo"],"abstract":"In this paper, we introduce a challenging new dataset, MLB-YouTube, designed\nfor fine-grained activity detection. The dataset contains two settings:\nsegmented video classification as well as activity detection in continuous\nvideos. We experimentally compare various recognition approaches capturing\ntemporal structure in activity videos, by classifying segmented videos and\nextending those approaches to continuous videos. We also compare models on the\nextremely difficult task of predicting pitch speed and pitch type from\nbroadcast baseball videos. We find that learning temporal structure is valuable\nfor fine-grained activity recognition.","url_abs":"http://arxiv.org/abs/1804.03247v1","url_pdf":"http://arxiv.org/pdf/1804.03247v1.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":"fine-grained-activity-recognition-in-baseball","repo_url":"https://github.com/piergiaj/mlb-youtube","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fine-grained-activity-recognition-in-baseball","repo_url":"https://github.com/jwwoody/mlb-deeplearning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fine-grained-activity-recognition-in-baseball","repo_url":"https://github.com/jwwoody/mlb-youtube","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"activity-detection","task_name":"Activity Detection"},{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"video-classification","task_name":"Video Classification"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[{"slug":"mlb-youtube-dataset","name":"MLB-YouTube Dataset","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.03247","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}