Feature Extraction of Foul Action of Football Players Based on Machine Vision

With the improvement of technology and tactics, the rhythm of football match is getting faster and faster, which leads to more intense competition behavior in a football match; the physical contact of both players is also increasing, and the frequency of fouls by football players is getting higher and higher. This leads to fouls by players. Because of the error of visual analysis, in the crowd of high-level football players, the traditional football players’ foul behavior feature extraction method has the problem of low precision of foul action feature extraction. This paper mainly studies the feature extraction of soccer players’ foul action based on machine vision. To solve these problems, this paper uses a machine vision-based football player foul action feature extraction method, using a machine vision system to obtain football player action image, based on threshold recognition algorithm to identify the football player’s foul action. Based on the recognition of the foul action image, the potential function sequence of the foul action sequence is established by the Harris 3D operator, and the characteristic data of football player foul action are filtered by the AdaBoost algorithm. The simulation results show that this method has high accuracy in identifying fouls in the range of high-level football players and effectively reduces the recognition error. The method proposed in this paper can effectively analyze the characteristics of foul action and help football clubs to develop more perfect tactics.