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Basketball technique action recognition using 3D convolutional neural networks.

Jingfei Wang1,2, Liang Zuo3, Carlos Cordente Martínez4

  • 1Physical Education Department, Northwestern Polytechnical University, Xi'an, 710129, Shaanxi, People's Republic of China. jingfei.wang@alumnos.upm.es.

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Summary
This summary is machine-generated.

This study introduces a new method using 3D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for accurate basketball action recognition. The model significantly improves technique identification for coaches and players.

Keywords:
3D convolutional neural networksAction recognitionBasketball techniqueLong short-term memory networksTemporal feature modeling

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Sports Analytics

Background:

  • Automated recognition of sports actions is crucial for performance analysis.
  • Basketball technique analysis traditionally relies on manual observation, which is time-consuming and subjective.

Purpose of the Study:

  • To develop an accurate and automated system for recognizing basketball technique actions using deep learning.
  • To enhance the identification of various actions within basketball games.

Main Methods:

  • Implementation of three-dimensional (3D) Convolutional Neural Networks (CNNs) combined with Long Short-Term Memory (LSTM) networks.
  • Utilized publicly available basketball action datasets (NTURGB+D, Basketball-Action-Dataset, B3D Dataset) with preprocessing techniques.
  • Employed optimization algorithms like adaptive learning rate adjustment and regularization to improve model performance.

Main Results:

  • The proposed 3D CNN-LSTM model achieved outstanding performance in basketball technique action recognition.
  • Demonstrated significant accuracy improvements: 15.1% over frame difference methods and 12.4% over optical flow methods.
  • Showcased strong robustness, achieving an average accuracy of 93.1% across diverse conditions.

Conclusions:

  • The developed method effectively captures the spatiotemporal relationships of basketball actions.
  • Provides a reliable technical assessment tool for basketball coaches and players.
  • Highlights the potential of deep learning for advanced sports analytics.