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Design of action detection system in wrestling match video based on 3D convolutional neural network

Design of action detection system in wrestling match video based on 3D convolutional neural network At present, there are some problems in motion detection in wrestling video at home and abroad, such as low detection accuracy and poor robustness. A motion detection system combining three-dimensional convolution neural network and recursive neural network is studied and designed. It uses three-dimensional convolution to obtain low-level feature code, then uses recursive memory module to obtain timing features, and finally completes motion detection according to timing features. Under the ratio of these three parameters, the accuracy of 3D-CNN convolutional neural network structure is higher than that of 2D-CNN. When the ratio of the influence factor of circular memory module P to that of circular memory module C is 1, the accuracy of 3D-CNN improves the fastest and the accuracy is close to 20%. The research results provide a new idea for the development of human motion detection and recognition technology. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Wireless and Mobile Computing Inderscience Publishers

Design of action detection system in wrestling match video based on 3D convolutional neural network

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1741-1084
eISSN
1741-1092
DOI
10.1504/ijwmc.2022.122483
Publisher site
See Article on Publisher Site

Abstract

At present, there are some problems in motion detection in wrestling video at home and abroad, such as low detection accuracy and poor robustness. A motion detection system combining three-dimensional convolution neural network and recursive neural network is studied and designed. It uses three-dimensional convolution to obtain low-level feature code, then uses recursive memory module to obtain timing features, and finally completes motion detection according to timing features. Under the ratio of these three parameters, the accuracy of 3D-CNN convolutional neural network structure is higher than that of 2D-CNN. When the ratio of the influence factor of circular memory module P to that of circular memory module C is 1, the accuracy of 3D-CNN improves the fastest and the accuracy is close to 20%. The research results provide a new idea for the development of human motion detection and recognition technology.

Journal

International Journal of Wireless and Mobile Computing – Inderscience Publishers

Published: Jan 1, 2022

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