  Recent advances in computer vision have made significant progress in tracking
and pose estimation of sports players. However, there have been fewer studies
on behavior prediction with pose estimation in sports, in particular, the
prediction of soccer fouls is challenging because of the smaller image size of
each player and of difficulty in the usage of e.g., the ball and pose
information. In our research, we introduce an innovative deep learning approach
for anticipating soccer fouls. This method integrates video data, bounding box
positions, image details, and pose information by curating a novel soccer foul
dataset. Our model utilizes a combination of convolutional and recurrent neural
networks (CNNs and RNNs) to effectively merge information from these four
modalities. The experimental results show that our full model outperformed the
ablated models, and all of the RNN modules, bounding box position and image,
and estimated pose were useful for the foul prediction. Our findings have
important implications for a deeper understanding of foul play in soccer and
provide a valuable reference for future research and practice in this area.
