Individual Action and Group Activity Recognition in Soccer Videos from a Static Panoramic Camera

Data and statistics are key to soccer analytics and have important roles in player evaluation and fan engagement. Automatic recognition of soccer events - such as passes and corners - would ease the data gathering process, potentially opening up the market for soccer analytics at non professional clubs. Existing approaches extract events on group level only and rely on television broadcasts or recordings from multiple camera viewpoints. We propose a novel method for the recognition of individual actions and group activities in panoramic videos from a single viewpoint. Three key contributions in the proposed method are (1) player snippets as model input, (2) independent extraction of spatio-temporal features per player, and (3) feature contextualisation using zero-padding and feature suppression in graph attention networks. Our method classifies video samples in eight action and eleven activity types, and reaches accuracies above 75% for ten of these classes.