Dance action recognition technology based on deep learning

Abstract

This study explores the dance movement recognition technology based on deep learning, and aims to improve the recognition accuracy of dance movements by constructing and evaluating deep learning models. This paper combs the basic concepts and principles of deep learning, and analyzes the latest progress of dance action recognition, its application in different scenes and the challenges it faces. Subsequently, the process of data collection, preprocessing, labeling and feature extraction is described in detail, and the role of data preprocessing and enhancement technology in improving model performance is emphatically discussed. Based on this, this paper designs a hybrid architecture combining convolutional neural network (CNN) and recurrent neural network (RNN), and the corresponding model optimization strategy, in order to achieve higher accuracy and efficiency when dealing with complex dance sequences. The experimental design part includes the process of model training, evaluation and verification, and comprehensively tests the model performance. The results show that the proposed model performs well in many dance action recognition tasks, with high accuracy and good generalization ability. This study provides a valuable reference for the development of dance movement recognition technology, and also opens up a new way for in-depth research and application in related fields.