Dance Action Recognition Model Based on Spatial Frequency Domain Features of Contour Images

Aiming at solving the problems of low recognition rate, low recognition efficiency and poor recognition effect in the current dance motion recognition methods that are affected by the surrounding environment, this study proposes a dance action recognition model based on the spatial frequency domain features of contour image. This study uses texture information to extract the dance action contour image, solve the feature vector of the contour image by the hypercomplex Fourier transform, and adopts the phase spectrum and energy spectrum transformation to smooth the contour image, so as to generate a saliency map, finally completes the extraction and preprocessing of dance action contour image. This paper distinguishes the high-frequency and low-frequency parts of dance action through the method of discrete cosine transform, calculates the number of pixels contained in the dance action images to be recognized, and extracts the spatial frequency domain features of contour image of dance action, builds the human posture model. This model realizes the dance action recognition by using the classifier to process the above-extracted feature vector and its label. The experimental result shows that the dance action recognition effect of this research model is good, and its recognition rate is high in different dance action types, and can effectively meet the needs of dance action recognition.