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A novel parameter dense three-dimensional convolution residual network method and its application in classroom

Xuan Li1, Ting Yang2, Ming Tang2

  • 1School of Foreign Language, Shangrao Normal University, Shangrao, China.

Frontiers in Neuroscience
|October 28, 2024
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Summary
This summary is machine-generated.

This study introduces a Dense 3D Convolutional Residual Network (D3DCNN_ResNet) for accurate student expression and behavior recognition in classrooms, enhancing educational quality analysis through computer vision.

Keywords:
SSD algorithmbehavior recognitionresidual networkthree-dimensional convolutional neural networkvideo sequence

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Educational Technology

Background:

  • Traditional classroom quality analysis methods are subjective and resource-intensive.
  • Computer vision (CV) offers a solution for objective, real-time classroom monitoring.
  • There is a need for accurate student expression and behavior recognition systems.

Purpose of the Study:

  • To propose a novel Dense 3D Convolutional Residual Network (D3DCNN_ResNet) for analyzing student expressions and behaviors.
  • To improve the rationality and accuracy of classroom quality analysis.
  • To leverage CV for enhanced teaching strategies via real-time student engagement feedback.

Main Methods:

  • Combined Single Shot Multibox Detector (SSD) with an improved D3DCNN_ResNet.
  • Utilized 3D convolution in spatial and temporal domains.
  • Incorporated residual blocks with dense connections for feature flow and network depth.

Main Results:

  • Achieved 97.94% accuracy for expression recognition (CK+ dataset).
  • Reached 98.86% accuracy for behavior recognition (KTH dataset).
  • Demonstrated efficient model training and improved recognition accuracy.

Conclusions:

  • The D3DCNN_ResNet effectively recognizes student expressions and behaviors.
  • The network's architecture enhances feature flow and reduces redundancy.
  • This technology is suitable for classroom quality analysis and improving teaching strategies.