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

## TL;DR

This paper introduces a new model for detecting and tracking table tennis objects using motion estimation to improve electronic referee accuracy in competitions.

## Contribution

A novel motion object detection and motion estimation model combining background subtraction and Kalman filtering for table tennis.

## Key findings

- The model achieved an average detection accuracy of 0.94 in a 20-category dataset.
- The model's average detection time was 103.9 ms with a low average loss value of 0.33.
- The model showed high trajectory prediction accuracy even with partially missing video information.

## Abstract

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.