School of Physical Education, Jianghan University, Wuhan City, 430000, China.
Department of Computer Science, Nanjing University, Nanjing City, 210000, China.
Sci Rep. 2024 Jan 2;14(1):255. doi: 10.1038/s41598-023-50056-w.
In order to realize the process of player feature extraction and classification from multi-frequency frame-changing football match images more quickly, and complete the tactical plan that is more conducive to the game, this paper puts forward a method for analyzing and judging the tactics of women's football team based on Convolutional Neural Network (CNN). By extracting the players' performance in recent training and competition from continuous video frame data, a multi-dimensional vector input data sample is formed, and CNN is used to analyze the players' hidden ability before the game and the players' mistakes in different positions on the field to cope with different football schedules. Before the formal test, 10 games of 2021-2022 UEFA Women's Champions League were randomly selected and intercepted to train the CNN model. The model showed excellent accuracy in the classification of image features of various football moves and goal angles, and the overall classification accuracy of each category exceeded 95%. The accuracy of classifying a single match is above 88%, which highlights the reliability and stability of the model in identifying and classifying women's football matches. On this basis, the test results show that: according to the analysis of players' personal recessive ability before the game, after model image recognition and comparison, the difference between the four scores of players' personal recessive ability with CNN mode and the manual score of professional coaches was smaller, and the numerical difference was within the minimum unit value, and the numerical calculation results were basically the same. According to the analysis of players' mistakes in different positions on the field, CNN was used to monitor the real-time mistakes. It was found that the two players in the forward position made the highest mistakes, and they were replaced by substitute players at 73.44 min and 65.28 min after the team scored and kept the ball, respectively. After the substitute players played, the team's forward position mistake rate decreased obviously. The above results show that CNN technology can help players get personal recessive ability evaluation closer to professional evaluation in a shorter time, and help the coaching team to analyze the real-time events better. The purpose of this paper is to help the women's football team complete the pre-match tactical training, reduce the analysis time of players' mistakes in the game, deal with different opponents in the game and improve the winning rate of the game.
为了更快地实现从多帧率变换足球比赛图像中提取球员特征并进行分类的过程，并完成更有利于比赛的战术计划，本文提出了一种基于卷积神经网络（CNN）的女足战术分析和判断方法。通过从连续视频帧数据中提取球员最近训练和比赛中的表现，形成多维向量输入数据样本，然后使用 CNN 分析比赛前球员的隐藏能力以及球员在场上不同位置的失误，以应对不同的足球赛程。在正式测试之前，随机选择并截取了 2021-2022 赛季欧冠女足联赛的 10 场比赛来训练 CNN 模型。该模型在各种足球动作和进球角度的图像特征分类方面表现出了优异的准确性，每个类别的整体分类准确性都超过了 95%。每场比赛的分类准确率都在 88%以上，这突出了该模型在识别和分类女足比赛方面的可靠性和稳定性。在此基础上，测试结果表明：根据比赛前球员个人隐性能力的分析，经过模型图像识别和比较，使用 CNN 模式和专业教练的人工评分对球员个人隐性能力的四个评分之间的差异更小，数值差异在最小单位值内，并且数值计算结果基本相同。根据球员在场上不同位置的失误分析，使用 CNN 进行实时监测。结果发现，两名前锋位置的球员失误率最高，在球队进球并控球后 73.44 分钟和 65.28 分钟分别被替补球员替换。替补球员上场后，球队前锋位置的失误率明显下降。以上结果表明，CNN 技术可以帮助球员在更短的时间内获得更接近专业评估的个人隐性能力评估，并帮助教练团队更好地分析实时事件。本文的目的是帮助女足球队完成赛前战术训练，减少比赛中球员失误的分析时间，应对比赛中的不同对手，提高比赛胜率。
