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Tracking and identifying players is an important problem in computer visionbased ice hockey analytics. Player tracking is a challenging problem since themotion of players in hockey is fast-paced and non-linear. There is alsosignificant player-player and player-board occlusion, camera panning andzooming in hockey broadcast video. Prior published research perform playertracking with the help of handcrafted features for player detection andre-identification. Although commercial solutions for hockey player trackingexist, to the best of our knowledge, no network architectures used, trainingdata or performance metrics are publicly reported. There is currently nopublished work for hockey player tracking making use of the recent advancementsin deep learning while also reporting the current accuracy metrics used inliterature. Therefore, in this paper, we compare and contrast severalstate-of-the-art tracking algorithms and analyze their performance and failuremodes in ice hockey.

Evaluating deep tracking models for player tracking in broadcast ice hockey video