一个全面的手球动态数据集,用于游戏情况分类
Omar A Mures1,2, Javier Taibo1, Emilio J Padrón1,3
1Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.
Data in brief
|December 26, 2023
概括
这项研究引入了一个新的手球数据集,标记了比赛情况,包括球员和球的动态,用于实时游戏分类. 研究人员可以使用这些数据进行游戏分析和自动广播.
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:
- 专业的手球分析需要详细的游戏情况数据.
- 现有的数据集可能缺乏全面的球员和球动态.
- 手球比赛的自动分类是一个新兴的研究领域.
研究的目的:
- 为了呈现一个新的,标记手球比赛情况的大规模数据集.
- 为了提供一个合成的,规范化游戏框架的表现,与玩家和球的动态.
- 为了促进实时手球比赛分类的研究.
主要方法:
- 对象检测和跟踪以提取球员,裁判和球的位置.
- 鸟眼视角转换用于相机不变数据规范化.
- 卡尔曼过以提高位置稳定性和导出速度.
- 专家使用自定义系统对游戏情况进行注释.
主要成果:
- 一个包含大约100万个数据的数据集,来自11个小时的手球录像.
- 七个不同的游戏情境类别标记:攻击,过渡,点球和停赛.
- 综合数据包括球员/裁判的位置和速度,为了一致性而规范化.
结论:
- 该数据集可用于各种应用:游戏分析,自动广播和总结.
- 它增强了对玩家动态在游戏情境分类中的作用的理解.
- 为推进体育分析和手球人工智能提供了宝贵的资源.
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