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ACA-Net: adaptive context-aware network for basketball action recognition.

Yaolei Zhang1, Fei Zhang2, Yuanli Zhou3

  • 1China Basketball College, Beijing Sport University, Beijing, China.

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Summary

This study introduces an Adaptive Context-Aware Network (ACA-Net) for recognizing basketball actions. ACA-Net achieves high accuracy, outperforming existing methods for intelligent action recognition in sports.

Keywords:
action recognitionadaptive context-awarenessbasketballlong short-term informationspace-channel information interaction

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Autonomous systems require real-time human activity analysis.
  • Basketball action recognition is challenging due to complex backgrounds, subtle action differences, and inconsistent lighting.
  • Accurate action recognition aids athletes, coaches, referees, and robotic applications.

Purpose of the Study:

  • To propose a novel Adaptive Context-Aware Network (ACA-Net) for precise basketball player action recognition.
  • To enhance feature extraction at temporal, spatial, and channel levels for improved accuracy.
  • To demonstrate the effectiveness of ACA-Net compared to existing methods.

Main Methods:

  • Developed an Adaptive Context-Aware Network (ACA-Net) incorporating a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module.
  • The LSTA module adaptively learns global and local temporal features.
  • The TSCI module learns spatial-channel interaction features to improve representation.

Main Results:

  • ACA-Net achieved 89.26% accuracy on the SpaceJam dataset and 92.05% on the Basketball-51 dataset.
  • The proposed network outperformed current mainstream action recognition methods.
  • Experimental results validate the effectiveness of the LSTA and TSCI modules.

Conclusions:

  • ACA-Net provides a robust solution for basketball action recognition, significantly improving accuracy.
  • The adaptable architecture of ACA-Net has potential applications in autonomous robotics and sports analytics.
  • This research contributes to advancing intelligent action recognition in complex, dynamic environments.