Analysis and Research on Technical and Tactical Action Recognition in Football Based on 3D Neural Network

Abstract

Abstract The current action recognition analysis method is easily affected by factors such as background, illumination, and target angle, which not only has low accuracy, but also relies on prior knowledge. Research on the identification and analysis of technical and tactical movements in football. According to the characteristics of football video, a multi-resolution three-dimensional convolutional neural network is constructed by combining the convolutional neural network and the three-dimensional neural network. The supervised training algorithm is used to update the network weights and thresholds, and the video images are input into the input layer. After the convolutional layer, sub-sampling layer and fully connected layer and other network layers to obtain action recognition results. The principal component analysis method is used to reduce the dimension to process the action data set, and the Fourier transform method is used to filter out the principal component noise. The experimental results show that the method can effectively identify the technical and tactical movements of athletes from complex football game videos, and analyze the applied technical and tactical strategies. The average value of accuracy, recall and precision of technical and tactical analysis is as high as 0.96, 0.97, and 0.95, and the recognition and analysis effect has significant advantages.