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基于深度学习的足球运动员技术动作行为的识别模型,使用PCA-LBP算法.

Hongtao Chen1, Zhengbai Lin2, Quan Xu3

  • 1School of Physical Education and Health, Yulin Normal University, Yulin, 537000, Guangxi, China.

Scientific reports
|April 21, 2025
PubMed
概括

这项研究通过使用本地二进制模式 (LBP) 的主要组件分析 (PCA) 来增强足球运动员的动作识别. PCA-LBP算法显著提高了对传统LBP的准确性,用于识别像和滴球这样的技术操作.

关键词:
深度学习是一种深度学习.当地二进制模式本地二进制模式主要组件分析的主要组件分析.足球运动员是足球运动员.技术行动行为识别技术行动行为识别

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科学领域:

  • 运动科学 运动科学 运动科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 针对足球的科学训练需要准确识别球员的技术行为和行为.
  • 深度学习在图像识别方面表现出色,为分析足球运动员的行为提供了潜力.
  • 对于足球动作识别的传统本地二进制模式 (LBP) 方法面临着高维数据和精度的挑战.

研究的目的:

  • 为了比较使用PCA-LBP算法与传统LBP算法对足球运动员技术动作识别的准确性.
  • 评估主要组件分析 (PCA) 在减少维度和提高足球动作识别准确性的有效性.

主要方法:

  • 在2020年比赛期间从200名足球运动员收集了技术动作识别数据.
  • 实现并将主要组件分析-本地二进制模式 (PCA-LBP) 算法与标准LBP算法进行比较.
  • 使用四个关键技术动作评估识别准确性:,滴球,停止和假动作.

主要成果:

  • 与传统的LBP算法相比,PCA-LBP算法在所有测试的操作中显示出更高的识别准确性.
  • 对于动动作,PCA-LBP准确度在50个识别实例时高出2%,在300个识别实例时高出24%.
  • 使用PCA-LBP方法对滴球,停止和假动作也观察到显著的准确性改善.

结论:

  • 使用PCA减少尺寸有效地提高了基于LBP的足球动作识别的准确性.
  • PCA-LBP算法提供了一种更精确的方法来分析和识别足球运动员的技术行为.
  • 这种方法为开发职业足球有针对性的培训计划提供了宝贵的技术援助.