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Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network.

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This study introduces a DNet model for volleyball action recognition using artificial intelligence. The model achieves 94.12% accuracy by optimizing convolutional structures and fusing spatial-temporal features, enhancing recognition and training speed.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Deep Learning

Background:

  • Human action recognition in continuous volleyball videos presents challenges in extracting effective spatial-temporal features.
  • Existing models may struggle with the complexity and continuous nature of sports movements.

Purpose of the Study:

  • To develop an accurate and efficient deep learning model for volleyball action standardization and recognition.
  • To address the limitations in extracting spatial-temporal features from continuous volleyball video streams.

Main Methods:

  • A novel DNet model is proposed, modifying the Inception module by replacing 5x5 convolutions with two 3x3 structures and 3x3 structures with 1x3 and 3x1 convolutions.
  • The model utilizes a heterogeneous, decoupled Inception module with internal parameter optimization for enhanced recognition accuracy.
  • Spatial features are extracted from RGB maps, and temporal features from optical flow maps, with a 1:1 weighted fusion.

Main Results:

  • The DNet model achieved a recognition accuracy of 94.12% on volleyball action videos and a homemade dataset in UCF101.
  • The proposed method demonstrated improved recognition capabilities compared to existing approaches.
  • The model also exhibited accelerated training speeds, indicating increased efficiency.

Conclusions:

  • The DNet model offers a significant advancement in artificial intelligence for sports action recognition, specifically for volleyball.
  • The optimized convolutional structures and feature fusion strategy effectively improve both accuracy and training efficiency.
  • This research provides valuable theoretical insights for the application of AI and deep learning in analyzing dynamic human movements.