Minimal data set definition.

<div> As a sport widely played around the world, the fairness and enjoyment of table tennis competitions have received increasing attention. Traditional table tennis referees rely on manual judgment, which has problems such as strong subjectivity and high misjudgment rate. Therefore, this study combines the background subtraction method and the Kalman filtering algorithm. It processes missing images in videos to propose a motion object detection and motion estimation model for table tennis events. The test results showed that the average loss value of the model was only 0.33, the average detection accuracy in the 20-category data set was 0.94, and the average detection time was 103.9 ms. In the simulation test, the model achieved the best trajectory prediction accuracy in both complete video images and partially missing information video images. The maximum difference in horizontal and vertical directions was 10.7 and 4.3 pixels, respectively, and the maximum error in three-dimensional coordinates was (3.3, 2.8, 2.1). The table tennis target detection and motion estimation model has high detection accuracy and stability, providing new ideas and methods for the development of electronic referee systems in table tennis competitions. </div>

Publication Details

Journal
Figshare
Published
2025-03-19
DOI
https://doi.org/10.1371/journal.pone.0319558.s001
Primary Topic
Human Pose and Action Recognition
Type
article
Field-Weighted Citation Impact
0.00
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article

Minimal data set definition.

Figshare
Human Pose and Action Recognition
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Minimal data set definition.

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Abstract

<div> As a sport widely played around the world, the fairness and enjoyment of table tennis competitions have received increasing attention. Traditional table tennis referees rely on manual judgment, which has problems such as strong subjectivity and high misjudgment rate. Therefore, this study combines the background subtraction method and the Kalman filtering algorithm. It processes missing images in videos to propose a motion object detection and motion estimation model for table tennis events. The test results showed that the average loss value of the model was only 0.33, the average detection accuracy in the 20-category data set was 0.94, and the average detection time was 103.9 ms. In the simulation test, the model achieved the best trajectory prediction accuracy in both complete video images and partially missing information video images. The maximum difference in horizontal and vertical directions was 10.7 and 4.3 pixels, respectively, and the maximum error in three-dimensional coordinates was (3.3, 2.8, 2.1). The table tennis target detection and motion estimation model has high detection accuracy and stability, providing new ideas and methods for the development of electronic referee systems in table tennis competitions. </div>

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Human Pose and Action Recognition
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