Still Rings Movements Recognition in Gymnastics Sport Based on Deep Learning

Abstract

The methods of detecting objects and tracking their movements are among the methods that are relied upon in many fields, whether medical or industrial, and others. One of these areas that will rely on deep learning meth-ods in discovering and distinguishing the player's movements is the sports field and is very useful in games in which the player's degree depends on the accura-cy of the performance of the movement, such as the gymnastics game, where it was applied to the static ring gymnastics game, where the distinction of move-ments was discovered the stability in this game is based on a convolutional neu-ral network. Models. The neural network was trained on five of the most im-portant stability movements in this game after creating the data set based on a set of videos of tournaments held in the period from 2016-2022, where an aver-age of 1500 images were obtained for each stability movement, which was di-vided into 80% for training and 20 % for testing, after training the convolution-al neural network model, it was applied to a group of video clips for different tournaments. Many criteria were adopted to measure the efficiency of the model after training and practical application, which showed the efficiency of the pro-posed system.