Research on optimization of table tennis hitting action based on image recognition technology

Abstract

In the process of table tennis batting action analysis, the analysis system that relies on modern digital image processing technology plays an increasingly important role. The new GoogleNet model, as a kind of auxiliary means, because of its intuitive, rapid, and other characteristics, can be very good to find out and optimize the drawbacks of batting action in the process of the game and help learners to master the batting essentials quickly. In this paper, the batch regression algorithm is used to transform the images of players’ batting actions during table tennis games, and the AM-Softmax algorithm in the Softmax classifier is used to construct the New GoogleNet model to build the batting action recognition and analysis model that contains both temporal and spatial flows. The main conclusions are as follows: the accuracy of the test results for forehand and left-handed ball attacks based on the New GoogleNet model is as high as 92% and 90%. Forehand and left-handed ball rolling accuracy was 87.5% and 85%, respectively. The optical flow optimization method resulted in a 0.4% and 1.4% increase in the accuracy of the experiments. In the two-stream fusion method with thresholds s=99% and s=1, the accuracy of optimization of the hitting action reached 89.8% and 91.4%, respectively. The accuracy in the averaging method was 95.9% when the optical flow threshold s=1. When the optical flow threshold s = 99% is used in the downscaling method, the accuracy is 93.5%. The results of this paper are of great significance for the recognition of batting movements during table tennis matches and the optimization and improvement of batting movements.