School of Physical Education, ShanXi University, Taiyuan 030006, Shanxi Province, China.
Department of Physical Education, The Graduate School of Dankook University, Yongin-si 16890, Gyeonggi-do, Republic of Korea.
Comput Intell Neurosci. 2021 Oct 27;2021:6882467. doi: 10.1155/2021/6882467. eCollection 2021.
With the advent of the information age, human demand for information is increasing day by day. The emergence of the concept of big data has triggered a new round of technological revolution, and visual information plays an important role in information. In order to obtain a better 3D model, this paper studies the reconstruction model of training motion 3D images based on a graphical neural network algorithm. This paper studies the problem of Sanda from the following two aspects. First, we try to apply two deep learning algorithms, graphical neural network and recurrent neural network, to the boxing movement recognition task and compare the effects with quadratic discriminant analysis and support vector machine. By comparing and analyzing the influence of different network structures on the deep learning algorithm, it is concluded that recurrent neural network has more practical application advantages than graph neural network in network structure parameter tuning.
随着信息时代的到来，人类对信息的需求日益增加。大数据概念的出现引发了新一轮的技术革命，视觉信息在信息中起着重要的作用。为了获得更好的 3D 模型，本文研究了基于图形神经网络算法的训练运动 3D 图像重建模型。本文从以下两个方面研究了散打的问题。首先，我们尝试将两种深度学习算法，图形神经网络和循环神经网络，应用于拳击运动识别任务，并与二次判别分析和支持向量机进行比较。通过比较和分析不同网络结构对深度学习算法的影响，得出循环神经网络在网络结构参数调整方面比图形神经网络具有更实际的应用优势。
