广播分析-评估基于视频的跟踪系统与受限的球员可见性。

IF 3.5
Manuel Bassek, Jonas Theiner, Ralph Ewerth, Daniel Memmert, Dominik Raabe
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引用次数: 0

摘要

近年来的进步使得从广播视频中生成跟踪数据成为可能。然而,不管这些系统的质量如何,由于运动员是不可见的,所以无法监控屏幕外的动作。本研究探讨足球转播视频中球员的知名度对足球典型数据分析程序的影响。为此,我们模拟了来自两个视频源的广播播放器跟踪数据(侦察源,SF;并在两个实验中将身体和战术表现指标的质量与官方球员跟踪数据(GT)进行比较。实验1分析球员可见度对总距离和高速覆盖距离的影响,实验2通过模板匹配研究球员可见度对战术编队探测的影响。结果显示,97%但不到50%的玩家活动分别出现在科幻小说和电视中。实验1表明,SF和TV的可视性显著影响体能比赛强度的评价。实验2表明,SF能见度对地层识别精度没有显著影响,而电视能见度有限导致地层识别精度略有下降。研究结果表明,虽然使用广播跟踪数据可以可靠地进行一些战术分析,但物理指标可能更容易受到丢失数据造成的不准确性的影响。虽然数据质量可以通过缺失球员轨迹的插值来提高,但研究人员和从业人员依赖于数据提供者在评估其数据是否足以完成手头任务方面的方法的透明度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Broadcast analytics - an evaluation of video-based tracking systems with constrained player visibility.

Advancements in recent years have enabled the generation of tracking data from broadcast videos. However, regardless of the quality of these systems, off-screen actions cannot be monitored since the athletes are not visible. The present study investigates the influence of player visibility in soccer broadcast videos on typical data analysis routines in soccer. To this end, we emulate broadcast player tracking data from two video sources (scouting feed, SF; broadcast television, TV) and compare the quality of physical and tactical performance metrics to the official player tracking data (GT) in two experiments. Experiment 1 analyzes the impact of player visibility on total distance and high-speed distance covered, while experiment 2 investigates its effect on tactical formation detection through template matching. The results show that overall 97% but less than 50% of player activity is visible in SF and TV, respectively. Experiment 1 indicates that visibility in SF and TV significantly affects the assessment of physical match intensity. Experiment 2 shows that SF visibility has no meaningful effect on formation recognition accuracy, while limited visibility in TV results in minor accuracy reductions. The findings suggest that while some tactical analysis can be reliably conducted using broadcast tracking data, physical metrics may be more susceptible to inaccuracies caused by missing data. Although data quality may be improved through interpolation of missing player trajectories, researchers and practitioners rely on transparency from data providers regarding their methods to assess the sufficiency of their data to the task at hand.

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