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

Abstract

Learning activities interactions between small groups is a key step in understanding team sports videos. Recent research focusing on team sports videos can be strictly regarded from the perspective of the audience rather than the athlete. For team sports videos such as volleyball and basketball videos, there are plenty of intra-team and inter-team relations. In this paper, a new task named Group Scene Graph Generation is introduced to better understand intra-team relations and inter-team relations in sports videos. To tackle this problem, a novel Hierarchical Relation Network is proposed. After 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 Graph. For 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 method. Moreover, the idea of our method can be directly utilized in another video-based task, Group Activity Recognition. Experiments show the priority of our method and display the link between the two tasks. Finally, 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.