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Updated: Jul 10, 2025

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A study on table tennis landing point detection algorithm based on spatial domain information.

Tao Ning1,2, Changcheng Wang1,2, Meng Fu1,2

  • 1State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology, Institute of Computer Science, Dalian Minzu University, Dalian, 116650, China.

Scientific Reports
|November 24, 2023
PubMed
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This summary is machine-generated.

This study introduces a real-time method for table tennis ball landing point detection using spatial information, reducing reliance on vision equipment. The approach achieves high accuracy and speed, even with low frame rates.

Area of Science:

  • Computer Vision
  • Robotics
  • Sports Analytics

Background:

  • Traditional computer vision for table tennis ball detection faces limitations due to heavy reliance on vision equipment and slow processing speeds.
  • Existing methods struggle with real-time performance and accuracy in dynamic environments.

Purpose of the Study:

  • To develop a real-time method for accurately determining table tennis ball landing points using spatial domain information.
  • To reduce dependency on specialized vision acquisition equipment and improve processing speed.

Main Methods:

  • Dynamic color thresholding to identify object centroids.
  • Target area thresholding and Euclidean distance for noise and interference reduction.
  • Keyframe extraction to optimize video processing.

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  • Four-frame difference slope and polygonal area determination for landing point and area detection.

    • Main Results:

    • Dynamic color thresholding achieved a detection speed of 45.3 frames per second (fps).
    • Keyframe extraction demonstrated over 93.3% accuracy in identifying landing point frames.
    • The proposed method achieved 78.5% overall accuracy for table tennis ball drop point detection in real-time.

    Conclusions:

    • The novel method enables accurate, real-time table tennis ball landing point detection.
    • This approach is effective even with low frame rate vision devices and in real-world conditions.
    • The method offers a viable solution for improving table tennis analysis and training systems.