Soccer: Who Has the Ball? Generating Visual Analytics and Player Statistics

The world of sports intrinsically involves fast and complex events that are difficult for coaches, trainers and players to analyze, and also for audiences to follow. In sports, talent identification and selection are imperative for the development of future elite level performers. Current scenarios involve word-of-mouth, coaches and recruiters scouring through hours of videos and many times manual annotation of these videos. In this paper, we propose an approach that automatically generates visual analytics from videos specifically for soccer to help coaches and recruiters identify the most promising talents. We use (a) Convolutional Neural Networks (CNNs) to localize soccer players in a video and identify players controlling the ball, (b) Deep Convolutional Generative Adversarial Networks (DCGAN) for data augmentation, (c) a histogram based matching to identify teams and (d) frame-by-frame prediction and verification analyses to generate visual analytics. We compare our approach with state-of-the-art approaches and achieve an accuracy of 86.59% on identifying players controlling the ball and an accuracy of 84.73% in generating the game analytics and player statistics.