Title: Combination of Lv-3DCNN algorithm in random noise environment and its application in aerobic gymnastics action recognition

Authors: Zhijun Chen; Shuang Guo

Addresses: College of Physical Education and Health, Chongqing Metropolitan College of Science and Technology, Chongqing, 402167, China ' College of Physical Education and Health, Chongqing Metropolitan College of Science and Technology, Chongqing, 402167, China

Abstract: Action recognition plays a vital role in analysing human body behaviour and has significant implications for research and education. However, traditional recognition methods often suffer from issues such as inaccurate time and spatial feature vectors. Therefore, this study addresses the problem of inaccurate recognition of aerobic gymnastics action image data and proposes a visualised three-dimensional convolutional neural network algorithm-based action recognition model. This model incorporates unsupervised visualisation methods into the traditional network and enhances data recognition capabilities through the introduction of a random noise perturbation enhancement algorithm. The research results indicate that the data augmented with noise perturbation achieves the lowest mean square error, reducing the error value from 0.3352 to 0.3095. The use of unsupervised visualisation analysis enables clearer recognition of human actions, and the algorithm model is capable of accurately recognising aerobic movements. Compared to traditional algorithms, the new algorithm exhibits higher recognition accuracy and superior performance.

Keywords: action recognition; aerobic gymnastics; Lv-3DCNN; random noise perturbation.

DOI: 10.1504/IJWET.2024.142225

International Journal of Web Engineering and Technology, 2024 Vol.19 No.3, pp.213 - 231

Received: 11 Dec 2023
Accepted: 28 Mar 2024
Published online: 14 Oct 2024 *

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