Automated full-game ice hockey analytics
自动化的全场冰球分析
基本信息
- 批准号:576637-2022
- 负责人:
- 金额:$ 6.56万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Alliance Grants
- 财政年份:2022
- 资助国家:加拿大
- 起止时间:2022-01-01 至 2023-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
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中自动生成分析,以模仿人类的能力。尽管流行文化给人的印象是工程师们已经创造出了近乎完美的算法,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个应用。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),人类无法有效地解释所有游戏活动和事件。为了获得全面的分析,计算机视觉可以用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。这些信息可以用来处理比赛视频,比人类操作员更有效地支持侦察对手,准备团队比赛计划,评估选秀,并为比赛做好准备。使用计算机视觉捕捉冰球分析的挑战包括比赛的速度,转播视频无法识别小冰球,球员/冰球的持续遮挡(由棋盘和其他球员),识别具有相似身材和相同制服的独特球员,以及有限的视频视野。我们的研究团队在冰面上球员位置的学习方法、捕获地面真实数据的工具、同时跟踪所有球员的运动,以及与一家已经向职业联盟提供数据的既有商业利益的公司合作方面取得了重大进展。我们的目标是推进国家的最先进的冰球分析,能够自动解释所有的比赛事件和团队战略为一个单一的广播冰球比赛视频。
项目成果
期刊论文数量(0)
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