Analyzing Hockey Broadcast Videos Using Machine Learning

世界各地的公共娱乐以体育为主，特别是在电视上。它向公众传递的一个突出问题是，在比赛结束后有很大的延迟，直到公众和球队可以获得分析和有用的评论。此外，这些数据的汇编、汇总和呈现在很大程度上是通过观看电视上的体育赛事视频来完成的。因此，目前可获得和感兴趣的数据量非常有限。潜在地，这些分析将成为体育产业非常有利可图的收入来源，也是运动队教练和经理的宝贵工具。显然，自动化的内容感知体育视频分析在这方面将非常有用。*最近(2018年5月)的一篇论文[1]非常详细地调查了这一活动领域。它发现“……文献中超过80%的论文涉及棒球、篮球、足球和网球。它只列出了三个(！)关于曲棍球主题的论文总数。对这一应用的研究提出了改变这种情况的建议。*使用计算机视觉和人工智能，这项研究的动机是两个社会因素：1)体育作为娱乐的普及，2)严重缺乏对体育数据的自动化收集和分析。由于曲棍球对加拿大人的重要性，我们讨论的运动是职业曲棍球。它强调并强调机器学习来“理解”和确定曲棍球分析。具体地说，重点是提供离线自动分析视频内容的能力，以检测和量化相关的时间和空间事件，在这种情况下，NHL播放曲棍球比赛的视频。我们将其称为内容感知曲棍球视频分析。这项建议的一个独特的技术特点是，我们将利用现成的视频和语音数据(隐藏字幕)来进行曲棍球比赛的内容分析。*研究计划将集中在以下相关项目上。项目#1：一个用于分析曲棍球广播视频的系统，主要使用图像、视频和语言描述视频中的对象、动作和事件(冰上发生了什么？)稍后，我们将讨论项目2：基于项目1，计算实用曲棍球分析的新方法，以支持教练和教练员。(从视频中可以收集到哪些实际数据？)。*所提出的方法可称为智能多模式视频内容分析(IMVCA)。也就是说，能够离线自动分析和解释视频和语言内容，目的是检测和量化特定的空间和时间曲棍球事件。在我们的案例中，我们利用了曲棍球比赛的广播视频，但实际上，该方法可以应用于解释任何包含隐藏字幕(CC)的视频。因此，它将成为改进视频分析方法的工具。

Public entertainment around the world is dominated by sports, particularly on television. A prominent issue with its delivery to the public is that there is a significant delay after a game until analytics and useful commentary are available to the public and the teams. Also, the compilation, summarization, and presentation of these data is largely done by humans watching videos of a sporting event on TV. Therefore, the amount of available and interesting data are currently very limited. Potentially, these analytics would be a very lucrative source of income for the sports industry, as well as a valuable tool for coaches and managers of sports teams. Clearly, automated content-aware sports video analysis would be extremely useful in this regard.***A recent (May, 2018) paper [1] surveys this field of activity in great detail. It found that “...more than 80% of papers in the literature addressed baseball, basketball, soccer and tennis. It lists only three(!) papers in total on the subject of hockey. The research in this application proposes to change this situation.***Using Computer Vision and Artificial Intelligence, this research is motivated by two societal factors: 1) The pervasiveness of sports as entertainment, and 2) a significant lack of automated collection and analysis of sports data. Because of the significance of hockey to Canadians, the sport we are addressing is professional hockey. It addresses and emphasizes Machine Learning to “understand” and determine hockey analytics. Specifically, the focus is on providing the capability of automatically analyzing video content offline to both detect and quantify relevant temporal and spatial events, in this case, NHL broadcast videos of hockey games.We refer to this as content-aware hockey video analysis. A unique technical feature of this proposal is that we will employ both readily available video and verbal data (Closed Captioning) to perform the content analysis of the hockey game.***The research program will focus on the following related projects. Project #1: A system for analyzing hockey broadcast videos, primarily employing pictorial, video and linguistic descriptions of the objects, actions, and events in the video (What is happening on the ice?). Later, we will address Project #2: Based on #1, new methods for computing practical hockey analytics to support coaches and trainers. (What practical data can be gleaned from the videos?).***The proposed approach can be referred to as Intelligent Multimodal Video Content Analysis (IMVCA). That is, the capability of automatically analyzing and interpreting both video and language content offline with the aim of detecting and quantifying specific spatial and temporal hockey events. In our case, we exploit broadcast videos of hockey games, but in fact, the methodology could be applied to interpret any video containing Closed Captioning (CC). As such it will be a vehicle for advancing methods for the analysis of videos.****** **