The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

The purpose is to minimize subjective errors in the manual analysis of volleyball game videos and improve the traditional Human Behavior Recognition (HBR) algorithms’ excessive calculation, high hardware requirements, and poor long-stream video modeling ability. Firstly, this paper expounds on the relevant theories. Secondly, a fusion Convolutional Neural Network (CNN)-based HBR model is implemented. It combines the two-stream CNN (TSCNN), Three-Dimensional (3D) CNN, and Long Short-Term Memory (LSTM) and gives full play to the LSTM’s long-term Dynamic Information Extraction (DIE) ability. Finally, the public dataset is selected to verify the model’s volleyball-game-video-oriented HBR performance. Here are the experimental results. (1) The optimal key parameters of the proposed fusion-CNN-based HBR model are determined as follows: the number of video segments is three, the average method is used for feature fusion, and then the HBR accuracy is the highest when the fusion ratio of spatial feature map and temporal feature map is 4:6, and the learning rate is 0.0014. (2) The average recognition accuracy of the proposed fusion-CNN-based HBR model on three different datasets is 4%, 2.7%, and 3% higher than other popular networks, respectively. The improvement effect of the model is remarkable, and it is suitable for studying Human Behavior Analysis (HBA) in volleyball match videos. Finally, the proposed HBR model can provide more accurate results for volleyball videos’ HBR, which is significant in promoting the rapid development of volleyball sports. The proposed model can classify and label videos and understand and describe video behaviors to simplify video processing procedures and save social resources.