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通过人类姿势估计和机器学习增强板球表现分析.

Hafeez Ur Rehman Siddiqui1, Faizan Younas1, Furqan Rustam2

  • 1Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan.

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概括
此摘要是机器生成的。

这项研究使用计算机视觉和机器学习,以99.77%的准确度预测板球击球击球. 随机森林算法显示了提高板球教练和球员表现的巨大潜力.

关键词:
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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:

  • 板球是全球第二大最受欢迎的体育运动,拥有25亿粉丝.
  • 板球中的击球要求基于动态游戏因素做出快速决策.
  • 计算机视觉和机器学习正在成为体育表现分析的强大工具.

研究的目的:

  • 开发和评估一种计算机视觉和机器学习方法,用于预测八个特定的板球击球.
  • 确定最有效的机器学习算法来准确预测中风.

主要方法:

  • 利用MediaPipe库从视频数据中提取播放器功能.
  • 训练并比较多个机器学习和深度学习算法:随机森林 (RF),支持向量机,k-最近邻居,决策树,线性回归和长短期记忆.
  • 使用精度和k倍交叉验证评估模型性能.

主要成果:

  • 随机森林 (RF) 算法实现了99.77%的峰值预测准确度.
  • 在中风预测方面,RF显著超过了所有其他测试过的算法.
  • 对于射频模型的K折交叉验证,准确率为95.0%,标准偏差为0.07.

结论:

  • 计算机视觉和机器学习技术在预测板球击球打击方面表现出高效.
  • 随机森林算法为准确的板球中风预测提供了一个强大的解决方案.
  • 这些发现为增强板球教练方法和提升球员表现提供了途径.