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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 三维基于骨架的动作识别 (3D SAR) 利用了骨架数据的优势.
  • 现有的调查主要集中在RGB数据上,对骨架数据的审查有限.
  • 深度学习方法被广泛应用,但从架构的角度缺乏全面的审查.

研究的目的:

  • 为了强调动作识别和3D骨架数据的重要性.
  • 提供使用深度学习架构的3D SAR技术的全面审查.
  • 解决关于基于深度学习的3D SAR审查的文献上的差距.

主要方法:

  • 基于四种基本深层架构的主流3D SAR技术的审查:循环神经网络 (RNNs),卷积神经网络 (CNNs),图形卷积网络 (GCNs) 和变压器.
  • 数据驱动的演示和详细讨论每个架构中的方法.
  • 分析主要的3D骨架数据集,包括NTU-RGB+D和NTU-RGB+D 120.

主要成果:

  • 基于核心深度学习架构的3D SAR方法的识别和分类.
  • 讨论基于骨架的动作识别的不同深度学习模型的优点和应用.
  • 在NTU-RGB+D和NTU-RGB+D120等基准数据集上的最新性能概述.

结论:

  • 这项调查是第一个全面讨论基于深度学习的动作识别使用3D骨架数据的调查.
  • 它提供了对3D SAR深度学习架构的基本理解.
  • 突出了关键的数据集和算法,为未来的研究提供了见解.