Application of big data technology in traditional dance video movement recognition research

Abstract

Abstract This paper proposes a multi-feature fusion approach for action recognition under big data technology with the goal of improving traditional dance video action recognition. By analyzing the basic method of dance action, the extraction process of dance action features is analyzed using both single-layer and hierarchical methods. Multi-feature fusion action recognition is chosen as the main method for action recognition. The image and audio features of the dance video are combined to improve the accuracy of recognizing dance actions. Use the optical flow algorithm to construct a histogram of the optical flow direction. The method’s feasibility is explored by applying the multi-feature fusion recognition method to traditional dance movement recognition. The results show that in traditional dance movement recognition performance, the performance of the method of multi-feature fusion recognition is improved by 7.6% compared to other traditional methods. The multi-feature fusion recognition method has more than 50% accuracy in recognizing different traditional dance movements and similar movements in terms of movement recognition accuracy. To a certain degree, this study enhances the efficiency of traditional dance movement recognition and conserves human and financial resources in dance movement recognition.