Abstract
Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Therefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. The SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. The network architecture is composed of application layer, control layer, and infrastructure layer. Based on this, the dimension of image sample set is reduced, and the edge detection operator in any direction is constructed. The image edge filter is realized by calculating the response and threshold of image edge by using lag threshold and nonmaximum suppression (NMS). The Hough transform algorithm is improved to optimize the detection range. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. In addition, the F1 test results of the proposed method are higher than those of the literature, and the convergence is better. Therefore, the performance of the proposed method is better.
Highlights
A large number of sports videos are collected in the process of sports training and teaching
The double convolution theory is used to segment the image of human action under high intensity, and the feature of human action is extracted. en, combined with the Gaussian distribution model, the obtained human motion image target and background and foreground information are processed to obtain the Gaussian distribution model of human motion image background, and the tracking trajectory of human motion image is obtained by Kalman filter
The previously mentioned methods cannot combine the transmission of image data with the interaction of features, which leads to the independence of the steps of image recognition, which takes longer time and has higher noise. erefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed
Summary
Received 26 November 2021; Revised 20 December 2021; Accepted 23 December 2021; Published 5 January 2022. Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Erefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. E SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. The F1 test results of the proposed method are higher than those of the literature, and the convergence is better. The F1 test results of the proposed method are higher than those of the literature, and the convergence is better. erefore, the performance of the proposed method is better
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