Tracking and Identification of Ice Hockey Players

Qiao Chen, Charalambos Poullis

Abstract: Due to the rapid movement of players, ice hockey is a high-speed sport that poses significant challenges for player tracking. In this paper, we present a comprehensive framework for player identification and tracking in ice hockey games, utilising deep neural networks trained on actual gameplay data. Player detection, identification, and tracking are the three main components of our architecture. The player detection component detects individuals in an image sequence using a region proposal technique. The player identification component makes use of a text detector model that performs character recognition on regions containing text detected by a scene text recognition model, enabling us to resolve ambiguities caused by players from the same squad having similar appearances. After identifying the players, a visual multi-object tracking model is used to track their movements throughout the game.