A Method for Identifying Sports Behaviors in Sports Adversarial Project Training Based on Image Block Classification under the Internet of Things

Abstract

Abstract There is a need to devise intelligent methods for motion-based image classification to produce accurate results for correct judgment. Existing methods cannot use the image block classification method to recognize the motion behavior of antagonistic sports training, and it is the need of the hour to devise newer methods. The recognition result is not ideal when using the traditional methods. This paper proposes an image block classification method for antagonistic sports training behavior recognition. It classifies the noise sources, quantifies the influence degree of each noise source effectively, and adjusts the noise factor in the denoising method for different noise sources and the specific weight of different noise sources. The adaptive denoising of different noise types of antagonistic sports training image sequences is realized. By using the combination of key frame template selection method and image segmentation method for behavior representation, the features are extracted well. The feature extraction is done on an image region according to the proportion of the number of foreground pixels in each block of the template to the number of pixels in the block. The feature vector is used to form the template. Aiming at the behavior representation and feature extraction based on the features of skeleton joint points, image block classification is used to extract the coordinates of skeleton joint points during human movement. K-means clustering is used to transform them into symbol sequences to represent behavior features that are used for antagonistic sports training behavior recognition. Simulation results show that the proposed method can obtain ideal recognition results and outperforms the existing methods.