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A vision-based system to support tactical and physical analyses in futsal

机译:一个基于视觉的系统,支持五人制足球战术和物理分析

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摘要

This paper presents a vision-based system to support tactical and physical analyses of futsal teams. Most part of the current analyses in this sport are manually performed, while the existing solutions based on automatic approaches are frequently composed of costly and complex tools, developed for other kind of team sports, making it difficult their adoption by futsal teams. Our system, on the other hand, represents a simple yet efficient dedicated solution, which is based on the analyses of image sequences captured by a single stationary camera used to obtain top-view images of the entire court. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to track them in every video frame. The system determines the distance traveled by each player, his/her mean and maximum speeds, as well as generates heat maps that describe players’ occupancy during the match. To present the collected data, our system uses a specially developed mobile application. Experimental results with image sequences of an official match and a training match show that our system provides data with global mean tracking errors below 40 cm, demanding on 25 ms to process each frame and, thus, demonstrating its high application potential.
机译:本文提出了一个基于视觉的系统来支持五人制足球队的战术和物理分析。当前这项运动的大部分分析都是手动执行的,而基于自动方法的现有解决方案通常由昂贵且复杂的工具组成,这些工具是为其他类型的团队运动开发的,因此很难被五人制足球队采用。另一方面,我们的系统代表一种简单而有效的专用解决方案,该解决方案基于对单个固定摄像机捕获的图像序列的分析,该摄像机用于获取整个球场的顶视图图像。我们使用自适应背景减法和斑点分析来检测播放器,并使用粒子过滤器在每个视频帧中对其进行跟踪。该系统确定每个球员的行进距离,他/她的平均速度和最大速度,并生成描述比赛过程中球员占用率的热图。为了展示收集的数据,我们的系统使用了专门开发的移动应用程序。通过官方比赛和训练比赛的图像序列进行的实验结果表明,我们的系统提供的数据具有低于40 cm的全局平均跟踪误差,需要25 ms才能处理每帧,因此证明了其较高的应用潜力。

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