An adaptive recognition method for take-off action images of back-style high jump based on feature extraction

Abstract

In the process of human movement, the behavioral characteristics of human body are complex and diverse. Thus, it is difficult to use accurate shape to represent the uncertainty of human behavior, which brings difficulties for 3D modeling in the state of motion. To solve this problem, an adaptive image recognition method based on feature extraction is proposed in this paper. Firstly, the image noise threshold was obtained by wavelet coefficient conversion. Then the wavelet coefficient matrix was constructed on the basis of decomposed image multi-resolution, and the image noise was removed according to the threshold. Based on this, Gaussian function in Canny method is introduced to obtain the edge coordinates of moving targets by convolution operation. After that, the image rotation and translation matrices were resolved according to the Murkowski distance to obtain the motion feature vectors. Finally, the hidden Markov model was constructed to segment the background and the target according to the feature sequence and the target edge coordinates, so as to realize the adaptive recognition of the take-off action images of the back-style high jump. Experimental results showed that this method had the advantages of high denoising effect, accurate feature extraction, error free edge division and accurate recognition result.