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Published on: December 24, 2014

Footwork recognition and trajectory tracking in track and field based on image processing.

Jiaju Zhu¹, Zhong Zhang², Runnan Liu³

¹School of Physical Education, Northeast Normal University, Changchun, 130024, China.

Scientific Reports

|March 29, 2025

Summary

This summary is machine-generated.

This study introduces an image processing technique using support vector machine (SVM) to accurately track and analyze athletic footwork. The method enhances performance and reduces injury risk for athletes in track and field.

Keywords:

Footwork recognition SIFT (scale-invariant feature transform) feature extraction SVM algorithm Track and field sports

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Area of Science:

Sports Science
Biomechanics
Computer Vision

Background:

Footwork accuracy is crucial for performance and injury prevention in track and field.
Traditional methods for footwork analysis lack precision and user acceptance.
Developing objective and accurate footwork analysis tools is essential for athletic training.

Purpose of the Study:

To develop and validate an image processing method for accurate identification and tracking of athletic footwork.
To improve the performance and reduce injury risk in track and field athletes through precise footwork analysis.
To assess the efficacy of the Support Vector Machine (SVM) algorithm in classifying and standardizing athletic movements.

Main Methods:

Extracted 13-second video frames from Olympic track and field competitions.
Utilized an image processing technique based on the Support Vector Machine (SVM) algorithm.
Tracked athlete footwork trajectories, extracted feature points, and categorized movements.
Standardized athlete behaviors based on extracted features and compared pre- and post-standardization performance.

Main Results:

The SVM algorithm demonstrated superior classification accuracy and recognition performance compared to other algorithms.
Image processing of standardized track and field movements led to performance improvements in all tested athletes (0.4-0.6).
The SVM-based image processing method proved effective and acceptable for athletic training applications.

Conclusions:

The SVM algorithm-based image processing method offers a reliable and effective approach for analyzing and improving athletic footwork.
This technique can significantly enhance track and field athlete performance and potentially reduce training-related injuries.
The developed method shows promise for wider application and extension in sports science and biomechanics analysis.