Recognition and Prediction Method of Music and Dance Movements Based on G-ResNet SPP and Attention Mechanism

Abstract

Aiming at the problems of difficult action recognition and low prediction accuracy in the process of music and dance movement. This paper proposes a music and dance motion recognition and prediction method based on G-ResNet SPP (Spatial Pyramid Pooling) and attention mechanism, so as to improve the accuracy of motion feature recognition. Firstly, the action recognition model is described, and the related theoretical basis and the construction of sampling function and weight function in action recognition are described. Secondly, the recognition method of the encoder-decoder design framework is described; finally, a group of music and dance sequences are used to verify the system, and the results show that the recognition effect of the G-ResNet + APP + ATTEN model has better performance in different experimental data sets.