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Convolutional neural network-based recognition method for volleyball movements.

Hua Wang1, Xiaojiao Jin2, Tianyang Zhang3

  • 1Physical Education Department, Xinxiang Institute of Engineering, Xinxiang, 453000, China.

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|August 3, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances deep learning for volleyball action recognition. Improved 3D networks accurately identify player movements, boosting teaching effectiveness in college physical education.

Keywords:
AccuracyComplexityConvolution neural networkMotion recognitionPhysical education curriculum

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Area of Science:

  • Computer Science
  • Sports Science
  • Artificial Intelligence

Background:

  • Traditional video analysis methods are insufficient for specific target analysis in sports like volleyball.
  • Intelligent processing of video data is crucial for analyzing diverse student movements in college physical education.
  • Research on deep learning for volleyball action recognition remains limited.

Purpose of the Study:

  • To address the limitations in current volleyball action recognition.
  • To improve the accuracy and efficiency of analyzing volleyball movements using deep learning.
  • To develop a more effective tool for college physical education using intelligent video analysis.

Main Methods:

  • Constructed a dedicated dataset for volleyball actions.
  • Improved a convolutional neural network (CNN) model.
  • Developed new neural network structures to enhance nonlinear expression and optimize input data.

Main Results:

  • The improved 3D network achieved an accuracy increase of 3.3% to 88.5% compared to the original.
  • Computational complexity was reduced by 33.6%.
  • The enhanced model demonstrates superior performance in recognizing volleyball actions.

Conclusions:

  • The improved deep learning model offers a more accurate and efficient solution for volleyball action recognition.
  • This technology has significant potential to enhance coaching and analysis in college physical education.
  • Further research in deep learning for sports action recognition is warranted.