A comprehensive dataset of soccer event images for advancing automatic recognition systems

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-03-22 DOI:10.1016/j.dib.2025.111518

Jamal Hussain Shah , Maira Afzal , Samia Riaz , Mussarat Yasmin , Seifedine Kadry , Fahad Ahmed Khokhar

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引用次数: 0

Abstract

This article presents a detailed overview of a dataset, created for in-depth analysis of soccer events. This dataset will serve as a foundation for researchers and practitioners in the field, providing a perspective on different soccer events under various views. This soccer dataset is designed to categorize soccer matches into various events and contains 187,151 instances divided across 14 groups. To make this dataset simple, it is separated into two main datasets. The first dataset is known as the “View-Based Dataset.” which is divided into four categories: Long view, Medium view, Short view, and Outer view, for a total of 137,196 images. The second dataset is the “Event-Based Dataset,” which has 10 separate classes that highlight multiple soccer events Red card, Spectator, Yellow card, Plenty stock, Player celebration, Offside, Goal attempt, Goal, and Free kick for a total of 38,728 images. Each class in both datasets helps to provide a full understanding of soccer events. This dataset can serve as a foundation for future video analysis studies, promoting progress in soccer analytics and related domains.

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一个全面的足球赛事图像数据集，用于推进自动识别系统

本文介绍了一个数据集的详细概述，该数据集是为深入分析足球赛事而创建的。该数据集将为该领域的研究人员和实践者提供基础，提供不同视角下不同足球赛事的视角。这个足球数据集旨在将足球比赛分类为各种事件，包含187,151个实例，分为14组。为了使这个数据集简单，它被分成两个主要的数据集。第一个数据集被称为“基于视图的数据集”。，它分为四类：长视图、中视图、近视图和外视图，总共有137,196张图片。第二个数据集是“基于事件的数据集”，它有10个独立的类，突出显示多个足球事件红牌、观众、黄牌、充足的股票、球员庆祝、越位、进球尝试、进球和任意球，总共有38,728张图像。两个数据集中的每个类都有助于提供对足球事件的全面理解。该数据集可以作为未来视频分析研究的基础，促进足球分析和相关领域的进步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.