Tracking and Identification of Ice Hockey Players

Due to the rapid movement of players, ice hockey is a highspeed 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. Experiments conducted with data collected from actual ice hockey games demonstrate the viability of our proposed framework for tracking and identifying players in real-world settings. Our framework achieves an average precision (AP) of 67.3 and a Multiple Object Tracking Accuracy (MOTA) of 80.2 for player detection and tracking, respectively. In addition, our team identification and player number identification accuracy is 82.39% and 87.19%, respectively. Overall, our framework is a significant advancement in the field of player tracking and identification in ice hockey, utilising cutting-edge deep learning techniques to achieve high accuracy and robustness in the face of complex and fast-paced gameplay. Our framework has the potential to be applied in a variety of applications, including sports analysis, player tracking, and team performance evaluation. Further enhancements can be made to address the challenges posed by complex and cluttered environments and enhance the system's precision.