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"玩玩":在一个标准化的空间中,手球和篮球比赛情况的数据集

Bruno Cabado^1,2, Bertha Guijarro-Berdiñas¹, Emilio J Padrón¹

¹Universidade da Coruña, CITIC Research Center, A Coruña 15071, Spain.

|January 23, 2025

概括

这项研究介绍了一组合成数据集,标记了手球和篮球比赛情况,这些数据集是从真实比赛录像中生成的. 该资源通过提供规范化的球员和球数据来帮助体育分析和机器学习研究.

科学领域:

运动分析运动分析
计算机视觉计算机视觉
机器学习机器学习

背景情况:

联合手球和篮球比赛为绩效分析提供了丰富的数据.
现有的数据集可能缺乏用于跨领域比较的标准化表示.
自动标记和数据规范化对于可扩展的体育分析至关重要.

研究的目的:

呈现一套新的合成数据集,标记手球和篮球的比赛情况.
为了提供对球员,裁判和球动态的规范化,表格式表示.
促进体育分析,体育人工智能和绩效预测方面的进步.

主要方法:

从手球和篮球比赛的真实视频中生成合成数据.
体育专家手动标记视频片段,并使用计算机视觉进行自动标记.
对象检测,跟踪和视角转换用于将数据规范化为"统一空间".

主要成果:

一个数据集包括308,805个手球框架和56,578个篮球框架.
七个不同的游戏情境类别:左/右攻击,反击,点球和停赛.
标准化表格数据,包括球员/裁判位置,速度和关键球场区域.

结论:

合成数据集为体育分析和机器学习提供了有价值的,可重复使用的资源.
标准化数据格式允许在不同场地和摄像机设置之间进行一致的分析.
潜在的应用包括提高球员表现,游戏策略和体育技术.

关键词:

球球球球球球游戏的情况,游戏的情况.规范化的正常化玩家玩家玩家玩家位置位置位置位置位置运动运动运动运动运动速度速度速度

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