Dance-Specific Action Recognition Method Based on Double-Stream CNN in Complex Environment

Abstract

Technology for dance-specific motion recognition is widely used in many industries, but Chinese research in this area is still in its early stages. Recognizing specific dance movements is the key to learning about and comprehending human actions and behaviors. The fault-tolerant feature of standardized sign language recognition is extended under the condition of small sample sizes, but the recognition accuracy remains a challenge. This issue needs to be resolved by fusing the essential details of particular dance movements. A dual-stream convolution neural network is suggested in this paper to investigate the recognition of particular dance movements. In this paper, a dual-stream convolution neural network is used to study the recognition of particular dance movements. The time spent by this algorithm gradually increases as the number of people in the image does, but only slightly. The algorithms proposed by Bergonzoni (2017) and Liu et al. (2021) both experience linear increases in running time as the population grows. In contrast, the running time of the algorithm in this study essentially increases negligibly. It has become a problem deserving in-depth study. Double-stream convolution neural network improves the practical value and technical complexity of dance motion automatic generation technology in art and cultural heritage protection, dance teaching, dance video retrieval, and dance arrangement.