期刊
SENSORS
卷 23, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/s23094506
关键词
shoot event prediction; soccer video; graph convolutional recurrent neural network; spatio-temporal information; Bayesian neural network
In soccer, it is crucial to quantitatively evaluate the performance of players and teams in order to improve tactical coaching and players' decision-making abilities. This paper proposes a novel method that utilizes players' spatio-temporal relations and prediction uncertainty to predict shoot event occurrences with greater accuracy and robustness. Experiments show that this method outperforms other methods in terms of prediction performance and emphasizes the significance of considering players' distances in the prediction accuracy.
In soccer, quantitatively evaluating the performance of players and teams is essential to improve tactical coaching and players' decision-making abilities. To achieve this, some methods use predicted probabilities of shoot event occurrences to quantify player performances, but conventional shoot prediction models have not performed well and have failed to consider the reliability of the event probability. This paper proposes a novel method that effectively utilizes players' spatio-temporal relations and prediction uncertainty to predict shoot event occurrences with greater accuracy and robustness. Specifically, we represent players' relations as a complete bipartite graph, which effectively incorporates soccer domain knowledge, and capture latent features by applying a graph convolutional recurrent neural network (GCRNN) to the constructed graph. Our model utilizes a Bayesian neural network to predict the probability of shoot event occurrence, considering spatio-temporal relations between players and prediction uncertainty. In our experiments, we confirmed that the proposed method outperformed several other methods in terms of prediction performance, and we found that considering players' distances significantly affects the prediction accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据