Motion recognition of football players based on deformable convolutional neural networks

In order to improve the accuracy of football player action recognition and the number of frames transmitted per second, a football player action recognition method based on deformable convolutional neural network is proposed. Firstly, the action images of football players are collected through binocular vision, and distortion correction and disparity calculation are performed on the images to improve their quality. Secondly, based on the collected athlete action images, the receptive field of the action images is calculated in two-dimensional convolution to extract football player action features. Finally, the extracted action features are input into the support vector machine to construct the optimal classification plane and complete the recognition of football player actions. The experimental results show that the action recognition accuracy of our method can reach up to 99.3%, and the transmission speed of our method is always stable at 24 frames per second or above.