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Updated: Aug 10, 2025

Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step

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Table Tennis Track Detection Based on Temporal Feature Multiplexing Network.

Wenjie Li¹, Xiangpeng Liu¹, Kang An¹

¹College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces a novel network for real-time table tennis trajectory analysis. The lightweight, high-precision model enhances objective data for competitive insights.

Keywords:

Transformer model deep learning feature reuse lightweight network motion trajectory object detection

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Area of Science:

Sports Science
Computer Vision
Machine Learning

Background:

Current table tennis analysis relies on subjective human observation, lacking objective data.
Objective data analysis is crucial for understanding player strategies and weaknesses in table tennis.

Purpose of the Study:

To develop an automated system for real-time table tennis trajectory extraction.
To provide objective data support for table tennis competition analysis.

Main Methods:

Proposed a target detection algorithm-based network for table tennis trajectory extraction.
Introduced a "feature store & return" module with Transformer model for efficient feature reuse and enhancement.
Focused on creating a lightweight yet accurate detection network.

Main Results:

Achieved 96.8% detection accuracy for table tennis and 89.1% for target localization.
The model is lightweight with a parameter size of only 7.68 MB.
Demonstrated a high detection frame rate of 634.19 FPS.

Conclusions:

The developed network is both lightweight and highly precise for table tennis detection.
The proposed model significantly outperforms existing methods in performance.
Enables objective, data-driven analysis of table tennis matches.