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Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets.

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|October 14, 2023
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Summary
This summary is machine-generated.

This study introduces an objective evaluation method for Taekwondo poomsae using a 3D convolutional neural network. The model ensures consistent action recognition across different viewpoints, improving fairness in Taekwondo competitions.

Keywords:
Taekwondo poomsaeaction recognitioncamera viewpointmartial artsskeletal data

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Area of Science:

  • Computer Science
  • Sports Science
  • Artificial Intelligence

Background:

  • Taekwondo poomsae evaluation lacks objectivity due to the absence of standardized methods.
  • Inconsistent scoring and fairness issues arise from subjective judging in Taekwondo poomsae.
  • Current evaluation methods struggle with viewpoint variations, impacting accuracy.

Purpose of the Study:

  • To develop an objective evaluation system for Taekwondo poomsae using artificial intelligence.
  • To enhance the consistency and fairness of Taekwondo poomsae scoring.
  • To create a robust action recognition model for Taekwondo poomsae adaptable to various viewpoints.

Main Methods:

  • Utilized a three-dimensional (3D) convolutional neural network (CNN) for action recognition.
  • Employed a full-body motion-capture suit to collect 3D skeleton data of Taekwondo poomsae.
  • Generated synthesized 2D skeletons from multiple viewpoints to create a diverse training dataset.

Main Results:

  • The proposed 3D CNN model demonstrated superior performance in recognizing Taekwondo poomsae actions.
  • The model achieved robust and consistent recognition irrespective of camera viewpoints.
  • Performance evaluation against 2D skeletons and RGB images confirmed the model's effectiveness.

Conclusions:

  • The 3D CNN-based model offers an objective and reliable method for Taekwondo poomsae evaluation.
  • This AI-driven approach can significantly improve fairness and consistency in Taekwondo competitions.
  • The model's viewpoint-invariant recognition capabilities address a key challenge in automated sports analysis.