  Tracking and identifying players is an important problem in computer vision
based ice hockey analytics. Player tracking is a challenging problem since the
motion of players in hockey is fast-paced and non-linear. There is also
significant player-player and player-board occlusion, camera panning and
zooming in hockey broadcast video. Prior published research perform player
tracking with the help of handcrafted features for player detection and
re-identification. Although commercial solutions for hockey player tracking
exist, to the best of our knowledge, no network architectures used, training
data or performance metrics are publicly reported. There is currently no
published work for hockey player tracking making use of the recent advancements
in deep learning while also reporting the current accuracy metrics used in
literature. Therefore, in this paper, we compare and contrast several
state-of-the-art tracking algorithms and analyze their performance and failure
modes in ice hockey.
