Single Image, Context Aware Action Estimation in Sports

Sports video analysis enables professional teams to prepare and practice more efficiently than ever before. It allows the team to work on their weak points and analyze upcoming opponents' strategies. As segmentation is a well researched topic, we assume a sports video where each frame is segmented into actors. We show that the location of the actors is correlated to their action. We develop a model to predict each actor's action in a context consistent and location aware manner. The set of context actors is an unordered set, thus we propose a permutation invariant structure for our classifier. We evaluate a location and a context aware network for single image sports action recognition on two datasets. One is an existing dataset of volleyball games. We present a new dataset for actor recognition in rugby. We compare the results to using only the image of the actor.