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Motion Object Detection Model for Electronic Referee Scoring in Table Tennis Events.

Xiaoke Li1, Lili Guo1

  • 1Faculty of Physical Education, Pingdingshan University, Pingdingshan, China.

Plos One
|March 19, 2025
PubMed
Summary

This study introduces an automated system for table tennis, using background subtraction and Kalman filtering for accurate ball tracking. The motion detection model enhances fairness in competitions by reducing human error.

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Area of Science:

  • Sports Technology
  • Computer Vision
  • Robotics

Background:

  • Table tennis competitions demand fair and enjoyable play, yet manual refereeing is subjective and error-prone.
  • Automated systems are needed to improve accuracy and consistency in officiating table tennis matches.

Purpose of the Study:

  • To develop a motion object detection and motion estimation model for table tennis events.
  • To address challenges with missing video frames in automated analysis.
  • To enhance the objectivity and accuracy of table tennis officiating.

Main Methods:

  • Combined background subtraction and Kalman filtering algorithms for video processing.
  • Developed a model for motion object detection and motion estimation specifically for table tennis.
  • Processed videos with missing frames to ensure robust tracking.

Main Results:

  • Achieved an average loss value of 0.33 and average detection accuracy of 0.94 across a 20-category dataset.
  • Demonstrated high trajectory prediction accuracy even with incomplete video data.
  • Reported maximum errors of 10.7 pixels (horizontal), 4.3 pixels (vertical), and (3.3, 2.8, 2.1) in 3D coordinates.

Conclusions:

  • The proposed model offers high detection accuracy and stability for table tennis.
  • This technology provides a foundation for developing advanced electronic referee systems.
  • The system has the potential to significantly improve fairness and reduce misjudgments in table tennis competitions.