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FootApp: an AI-Powered System for Football Match Annotation
arXiv - CS - Systems and Control Pub Date : 2021-03-04 , DOI: arxiv-2103.02938
Silvio Barra, Salvatore M. Carta, Alessandro Giuliani, Alessia Pisu, Alessandro Sebastian Podda, DanieleRiboni

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players' and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics, and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario.

中文翻译:

FootApp:一种用于足球比赛注解的AI驱动系统

近年来,科学和工业研究对获取大型带注释的数据集以训练人工智能算法以解决不同领域中的问题的兴趣日益增长。在这种情况下,我们观察到,甚至足球数据市场也已大幅增长。对足球比赛的分析依赖于对单个球员和球队行为的标注,以及球员的运动表现。因此,对足球事件进行细粒度注释是一项非常昂贵且容易出错的任务。现有的大多数用于足球比赛注解的半自动工具都依赖于相机和计算机视觉。但是,这些工具在捕获团队动态以及提取在摄像机框架中不可见的球员的数据方面是不足的。为了解决这些问题,在本手稿中,我们介绍FootApp,这是一种基于AI的足球比赛注释系统。首先,我们的系统依赖于先进的混合用户界面,该界面既利用语音交互又利用触摸交互。其次,通过将机器学习算法应用于从玩家佩戴的惯性传感器收集的数据来捕获并处理玩家的运动表现。然后使用人工智能技术检查所生成标签的一致性,包括与玩家身体活动有关的标签,以自动识别注释错误。值得注意的是,我们实现了所提出系统的完整原型,并进行了实验以证明其在实际应用场景中的有效性。我们的系统依赖于先进的混合用户界面,该界面既利用语音交互又利用触摸交互。其次,通过将机器学习算法应用于从玩家佩戴的惯性传感器收集的数据来捕获并处理玩家的运动表现。然后使用人工智能技术检查所生成标签的一致性,包括与玩家身体活动有关的标签,以自动识别注释错误。值得注意的是,我们实现了所提出系统的完整原型,并进行了实验以证明其在实际应用场景中的有效性。我们的系统依赖于先进的混合用户界面,该界面既利用语音交互又利用触摸交互。其次,通过将机器学习算法应用于从玩家佩戴的惯性传感器收集的数据来捕获并处理玩家的运动表现。然后使用人工智能技术检查所生成标签的一致性,包括与玩家身体活动有关的标签,以自动识别注释错误。值得注意的是,我们实现了所提出系统的完整原型,并进行了实验以证明其在实际应用场景中的有效性。然后使用人工智能技术检查所生成标签的一致性,包括与玩家身体活动有关的标签,以自动识别注释错误。值得注意的是,我们实现了所提出系统的完整原型,并进行了实验以证明其在实际应用场景中的有效性。然后使用人工智能技术检查所生成标签的一致性,包括与玩家身体活动有关的标签,以自动识别注释错误。值得注意的是,我们实现了所提出系统的完整原型,并进行了实验以证明其在实际应用场景中的有效性。
更新日期:2021-03-05
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