The world of sports intrinsically involves fast and complex events that are difficult for coaches, trainers and players to analyze, and also for audiences to follow. In fast paced team sports such as soccer, keeping track of all the players and analyzing their performance after every match are very challenging. Current scenarios for identifying the best talents in soccer involve word-of-mouth and coaches/recruiters scouring through hours of manually annotated videos. This is a very expensive and laborious process and also biased by the nature of the recruiters. To alleviate these problems, this paper proposes an automated system that can detect, track, classify the teams of multiple players and identify the player controlling the ball in a video. The system generates three very important tactical statistics for a player: 1) duration of ball possession, 2) number of successful passes and 3) number of successful steals. This is done by training Convolutional Neural Networks (CNNs) to (a) localize and track the players on the field, (b) classify the team of a detected player, (c) identify the player controlling the ball and (d) pooling all the information extracted from (a), (b), and (c) to generate the statistics of players. To overcome the problem that the features learned from specific soccer matches do not necessarily generalize across different soccer matches, the paper proposes minimal amount of match-specific annotation and data augmentation, using a variant of Deep Convolutional Generative Adversarial Networks (DCGAN) to improve the accuracy. Experimental results and ablation studies show that the proposed approach outperforms the state-of-the-art approaches in terms of accuracy and processing speed.

中文翻译：

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用于从视频中生成单个足球运动员战术表现统计数据的自动化系统


体育世界本质上涉及快速而复杂的赛事，教练、训练员和运动员都很难分析这些赛事，观众也很难跟上。在足球等快节奏的团队运动中，跟踪所有球员并在每场比赛后分析他们的表现非常具有挑战性。当前识别足球领域最佳人才的方案涉及口碑传播和教练/招聘人员在数小时的手动注释视频中进行搜索。这是一个非常昂贵和费力的过程，而且招聘人员的性质也存在偏见。为了缓解这些问题，本文提出了一种自动化系统，可以检测、跟踪、分类多个球员的球队，并识别视频中控球的球员。该系统为球员生成三个非常重要的战术统计数据：1）控球时间，2）成功传球次数和3）成功抢断次数。这是通过训练卷积神经网络 (CNN) 来完成的：(a) 定位并跟踪场上的球员，(b) 对检测到的球员的球队进行分类，(c) 识别控制球的球员，以及 (d) 汇集所有球员从（a）、（b）和（c）中提取的信息来生成玩家的统计数据。为了克服从特定足球比赛中学到的特征不一定能泛化到不同足球比赛的问题，本文提出了最少量的特定于比赛的注释和数据增强，使用深度卷积生成对抗网络（DCGAN）的变体来改进准确性。实验结果和消融研究表明，所提出的方法在准确性和处理速度方面优于最先进的方法。