Dance Action Target Recognition and Detection System Based on Machine Vision

In dance teaching, traditional motion recognition methods are limited by subjective judgment and visual blind spots, making it difficult to accurately capture complex and changeable dance movements. This study proposes a dance motion target recognition and detection system based on machine vision, which achieves accurate analysis of dance movements through image acquisition, background noise reduction, posture feature extraction and similarity algorithm. Experimental results show that the training and detection effects of this system are about 7 % higher than those of traditional methods. The system can effectively track changes in movement amplitude, intensity and continuity, provide an objective and accurate feedback mechanism for dance teaching, and significantly improve the scientificity and efficiency of dance training.