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Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget

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This study introduces a deep learning-based training method for freestyle skiing U-shaped venue skills, utilizing multi-target tracking and convolutional neural networks. The new method significantly improves athlete skill scores by 14.48% compared to traditional approaches.

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Area of Science:

  • Sports Science
  • Artificial Intelligence
  • Computer Vision

Background:

  • Freestyle skiing U-shaped venue skills demand high athlete proficiency.
  • Traditional training methods may lack scientific optimization.
  • Advancements in deep learning offer opportunities for enhanced sports training.

Purpose of the Study:

  • To develop a scientific training method for freestyle skiing U-shaped venue skills.
  • To integrate multi-target tracking algorithms with deep learning for motion capture.
  • To improve athlete skill recognition and performance through advanced technology.

Main Methods:

  • Combined convolutional neural networks (CNNs) and multi-target tracking algorithms for human action recognition.
  • Utilized Long Short-Term Memory (LSTM) modules for analyzing freestyle skiing skills.
  • Designed and conducted multi-target tracking dataset experiments and model updating experiments.

Main Results:

  • The developed deep learning-based training method demonstrated a 14.48% improvement in skill scores compared to traditional methods.
  • Experimental analysis and optimization refined the training approach.
  • Professional students reported high satisfaction with the new training method.

Conclusions:

  • The proposed deep learning framework, incorporating multi-target tracking, is effective for freestyle skiing U-shaped venue skills training.
  • This approach offers a more scientific and data-driven method for skill enhancement.
  • The findings suggest a promising direction for optimizing athletic training through AI.