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.

翻译：跟踪和识别玩家是基于计算机视觉的冰球曲棍球分析中的一个重要问题。 玩家跟踪是一个具有挑战性的问题,因为玩家在曲棍球中的运动速度快且非线性。 在曲棍球广播视频中,也有重要的玩家和玩家板隔离、照相机穿透和放大。 先前发表的研究利用手工制作的功能进行玩家跟踪,以探测和重新识别玩家。 尽管在跟踪曲棍球运动员方面存在着商业解决方案,但根据我们的知识,没有使用网络结构、培训数据或性能指标,也没有公开报告。 目前没有出版的曲棍球运动员工作,在跟踪利用最近深层学习的进展的同时,也报告文献中目前使用的准确度指标。 因此,在本论文中,我们比较和对比了几种最先进的跟踪算法,并分析了冰球队的性能和失败模式。