{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T16:04:48Z","timestamp":1709395488740},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683560","type":"print"},{"value":"9781643683577","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:00:00Z","timestamp":1669248000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,24]]},"abstract":"Most group activity recognition models focus mainly on spatio-temporal features from the players in sports games. Often they do not pay enough attention to the game object, which heavily affects not only individual action but also a group activity. We propose a new group activity recognition model for sports games that incorporates players\u2019 motion information and game object positional information. The proposed method uses a transformer encoder for temporal feature extraction and a \u2019simple\u2019 conventional convolutional neural network for extracting spatial features and fusing them with the relative ball position-embedded features. The experimental results show that our model achieved comparable results to state-of-the-art methods on the Volleyball dataset by using only one transformer encoder block and the ball position.<\/jats:p>","DOI":"10.3233\/faia220435","type":"book-chapter","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T09:27:29Z","timestamp":1669368449000},"source":"Crossref","is-referenced-by-count":1,"title":["Ball Position Feature Embedded Group Activity Recognition Model for Team Sport Games"],"prefix":"10.3233","author":[{"given":"Ankhzaya","family":"Jamsrandorj","sequence":"first","affiliation":[{"name":"Department of Human Computer Interface & Robotics Engineering, University of Science & Technology, South Korea"}]},{"given":"Vanyi","family":"Chao","sequence":"additional","affiliation":[{"name":"Department of AI Robotics, University of Science & Technology, South Korea"}]},{"given":"Yin May","family":"Oo","sequence":"additional","affiliation":[{"name":"Department of AI Robotics, University of Science & Technology, South Korea"}]},{"given":"Kyung-Ryoul","family":"Mun","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence, Korea Institute of Science and Technology, South Korea"}]},{"given":"Jinwook","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence, Korea Institute of Science and Technology, South Korea"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220435","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T09:27:30Z","timestamp":1669368450000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220435"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,24]]},"ISBN":["9781643683560","9781643683577"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220435","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,24]]}}}