Automated full-game ice hockey analytics

计算机视觉是一个研究领域，涉及创建能够解释场景的算法。一个关键的挑战是从数字视频自动生成分析，以模仿人类的能力。尽管流行文化给人的印象是，工程师创造了近乎完美的算法，但现实是，我们仍然面临着创造高效、有效和一致的解决方案的挑战，即使是针对专门的应用程序。从团队运动的广播视频中生成分析就是这样一种应用。对于冰球，人类捕获的分析通常只关注以冰球为中心的事件(例如，进球、射门)，并且人类不可能有效地解释所有比赛活动和事件。为了获取全面的分析，可以开发计算机视觉来解释所有玩家的活动，然后使用这些信息来解释游戏事件。在支持侦察对手、准备团队比赛计划、评估选秀和为球员做好比赛准备方面，此类信息可用于处理游戏视频，其效率远远高于人工操作员。使用计算机视觉来捕捉曲棍球分析的挑战涉及到比赛的速度、无法播放视频来识别小冰球、球员/冰球(被板球和其他球员)持续遮挡、识别具有相似体型和相同球衣的独特球员，以及视频的有限视野。我们的研究团队在了解冰上球员位置的方法、捕获地面真实数据的工具、同时跟踪所有球员的移动以及与一家已向职业联赛提供数据的既得利益公司的合作方面取得了巨大进展。我们的目标是推进最先进的曲棍球分析，以便能够自动解读单个转播的冰球比赛视频的所有比赛事件和球队战略。

Computer vision is a research field that involves creating algorithms capable of interpreting scenes. A key challenge is the automatic generation of analytics from digital video to mimic the ability of humans. Although pop culture gives the impression that engineers have created such algorithms to near perfection, the reality is that we are still challenged to create solutions that are efficient, effective, and consistent even for dedicated applications. Generating analytics from broadcast video of team sports is one such application. For ice hockey, human captured analytics typically focus only on puck-centric events (e.g., goals, shots) and it is not feasible for humans to efficiently interpret all game activities and events. To capture comprehensive analytics, computer vision can be developed to interpret all player activities and then use this information to interpret game events. Such information can be used to process game videos far more efficiently than a human operator in support of scouting opponents, preparing team game plans, assessing draft selections, and preparing players for games. The challenges with using computer vision to capture hockey analytics involve the speed of the game, the inability for broadcast video to identify the small puck, the persistent occlusions of players/pucks (by the boards and other players), identifying unique players that have similar builds and same uniforms, and limited field-of-view of the video. Our research team has made strong progress in methods to learn locations of players on the ice, tools for capturing ground truth data, tracking all player movements simultaneously, and working in partnership with a company with a vested business interest that already provides data to professional leagues. Our objective is to advance the state-of-the-art in hockey analytics to be able to automatically interpret all game events and team strategy for a single broadcast ice hockey game video.