Football Event Classification Using Convolutional Autoencoder and Multilayer Extreme Learning Machine

Hashmi, Mohammad Farukh; Bellare, Tejas Bhat; Suresh, Ankith; Naik, Banoth Thulasya

doi:10.1109/lsens.2022.3209366

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…As the first sport in the world, football video annotation and analysis are a time-consuming task. Once it is automated, it will bring benefits to coaches, players and spectators 6 , 7 . In modern football environment, non-contact and camera-based multi-player detection and tracking has become a reality, and automatic motion recognition can improve players’ performance through symbol analysis 8 – 10 .…”

Section: Introductionmentioning

confidence: 99%

Tactics analysis and evaluation of women football team based on convolutional neural network

Shen,

Tan,

et al. 2024

Sci Rep

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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.

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