Social Network Approach to Analysis of Soccer Game

Video understanding has been an active area of research, where many articles have been published on how to detect and track objects in videos, and how to analyze their trajectories. These methods, however, only provided heuristic low level information without providing a higher level understanding of global relations within the whole context. This paper presents a new way to provide such understanding using social network approach in soccer videos. Our approach considers representing interactions between the objects in the video as a social network. This network is then analyzed by detecting small communities using modularity, which relates social interaction. Additionally, we analyze the centrality of nodes which provides importance of individuals composing the network. In particular, we introduce five centralities exploiting directed and weighted social network. The partitions of the resulting social network are shown to relate to clusters of soccer players with respect to their role in the game.