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Learning group interaction for sports video understanding from a perspective of athlete

Frontiers of Computer Science 2024年第4期18卷 175-188页

作者： Rui HE Zehua FU Qingjie LIU Yunhong WANG Xunxun CHEN Intelligent Recognition and Image Processing(IRIP)Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Hangzhou Innovation Institute Behang UniversityHangzhou 310051China National Computer Network Emergency Response Technical Team/Coordination Center of China(CNCERT or CNCERT/CC) Beijing 100029China

Learning activities interactions between small groups is a key step in understanding team sports *** research focusing on team sports videos can be strictly regarded from the perspective of the audience rather than the *** team sports videos such as volleyball and basketball videos,there are plenty of intra-team and inter-team *** this paper,a new task named Group Scene Graph Generation is introduced to better understand intra-team relations and inter-team relations in sports *** tackle this problem,a novel Hierarchical Relation Network is *** all players in a video are finely divided into two teams,the feature of the two teams’activities and interactions will be enhanced by Graph Convolutional Networks,which are finally recognized to generate Group Scene *** evaluation,built on Volleyball dataset with additional 9660 team activity labels,a Volleyball+dataset is proposed.A baseline is set for better comparison and our experimental results demonstrate the effectiveness of our ***,the idea of our method can be directly utilized in another video-based task,Group Activity *** show the priority of our method and display the link between the two ***,from the athlete’s view,we elaborately present an interpretation that shows how to utilize Group Scene Graph to analyze teams’activities and provide professional gaming suggestions.

关键词： group scene graph group activity recognition scene graph generation graph convolutional network sports video understanding

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