Spectator Excitement Detection in Small-scale Sports Events

Detection of the excitement of spectators in sports is useful for various applications such as automatic highlight generation and automatic video editing. Therefore, spectator analysis has been widely studied. The two main approaches used for this include holistic and object-based approaches. Holistic approaches have been applied in most previous works, however, they do not work in small-scale games, where there are fewer spectators compared to those of large-scale games. In this work, we propose a method for detecting the state of excitement of spectators in small-scale games using an object-based approach. To evaluate our method, we build our own datasets consisting of both spectator and player videos. Experimental results show that our method outperforms a holistic baseline method and allows excitement detection of individual spectators.