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A fully automatic method for segmentation of soccer playing fields.

Carlos Cuevas¹, Daniel Berjón², Narciso García²

¹Grupo de Tratamiento de Imágenes (GTI), Information Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid (UPM), 28040, Madrid, Spain. carlos.cuevas@upm.es.

Scientific Reports

|January 26, 2023

Summary

This summary is machine-generated.

This study presents a new method for segmenting soccer playing fields in images. The approach achieves high-quality results, even in challenging lighting, improving upon existing strategies.

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Area of Science:

Computer Vision
Image Analysis
Sports Technology

Background:

Accurate soccer playing field segmentation is crucial for various sports analytics applications.
Existing methods face challenges with varying grass shades and complex lighting conditions.

Purpose of the Study:

To propose a robust strategy for segmenting soccer playing fields in images.
To enhance the accuracy and reliability of playing field detection in diverse scenarios.

Main Methods:

A novel strategy combining pixel-level green chromaticity analysis and chromatic distortion analysis.
Utilizing full-color information for precise green area identification.
Implementing an automatic region-level post-processing block to refine segmentation.

Main Results:

High-quality playing field segmentation achieved across diverse images, including those with challenging lighting.
Demonstrated superior performance compared to leading state-of-the-art segmentation strategies.
Effective handling of variations in grass shades and lighting conditions.

Conclusions:

The proposed strategy offers a reliable and effective solution for soccer playing field segmentation.
This method is suitable for integration into a wide range of soccer image analysis applications.
The approach significantly advances the state-of-the-art in sports image analysis.