The invention discloses a tennis
action recognition method and device based on
deep learning, and the method comprises the steps: detecting and positioning a tennis hitting moment through an acoustic event, extracting a certain number of frames, and inputting the frames into an
action recognition network; the method comprises the following steps: performing spatial
feature extraction on each frame in a to-be-detected tennis skill action
video sequence by using a
convolutional neural network, capturing features related to object deformation by using deformable
convolution, enhancing the spatial features through channel attention, focusing key features, and obtaining feature vectors capable of representing player movement information; and inputting the spatial
feature vector of each frame into Transform to calculate the correlation of the spatial
feature vector in time, simulating the deviation degree between different frame actions by using relative position coding, enhancing the correlation between different frames, and finally performing technical action classification by using a multi-layer
perceptron as a classifier. According to the method, the hitting moment is positioned through acoustic event detection, space information and
time information are processed separately in combination with a
convolutional neural network and a Transform, and a multi-layer
perceptron is used as a classifier. By means of the method, the skilled actions used by the tennis player can be efficiently and rapidly recognized.