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Tennis player actions dataset for human pose estimation.

Chun-Yi Wang1, Kalin Guanlun Lai2, Hsu-Chun Huang3

  • 1Office of Physical Education, National Taichung University of Science and Technology, Taichung, Taiwan.

Data in Brief
|July 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new tennis dataset for human pose estimation. It enables AI models to train and validate key tennis movements like forehand and backhand shots.

Keywords:
COCOHuman posture recognitionKeypoint detectionPose estimationSports TechnologyTennis action

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Area of Science:

  • Sports Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Integrating technology in sports training is crucial for performance enhancement.
  • Deep learning advancements have significantly improved human pose estimation capabilities.
  • Existing datasets may not adequately cover specific sport-related movements.

Purpose of the Study:

  • To create a comprehensive dataset for training human pose estimation models in tennis.
  • To facilitate the development of AI-powered tools for tennis player analysis and training.
  • To support research in both the sports science and computer vision fields.

Main Methods:

  • Collected videos of individuals playing tennis.
  • Segmented videos into frames and annotated key tennis movements.
  • Utilized COCO-Annotator for human skeleton joint labeling, generating JSON files.
  • Combined annotated data with classified images to form the final dataset.

Main Results:

  • Developed a novel dataset containing labeled tennis actions.
  • The dataset includes annotations for forehand shots, backhand shots, ready positions, and serves.
  • The dataset is suitable for training and validating deep learning models like OpenPose.

Conclusions:

  • The created dataset is a valuable resource for the tennis community and human pose estimation research.
  • This dataset can drive innovation in AI applications for sports training and performance analysis.
  • It provides a foundation for developing more sophisticated tennis coaching and analysis tools.