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双跳动动作控制器2:多视图手姿势识别的强大数据集

Manuel Gil-Martín1, Marco Raoul Marini2, Rubén San-Segundo3

  • 1Grupo de Tecnología del Habla y Aprendizaje Automático (T.H.A.U. Group), Department of Electrical Engineering, Information Processing and Telecommunications Center, E.T.S.I. de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain. manuel.gilmartin@upm.es.

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|October 9, 2024
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概括
此摘要是机器生成的。

研究人员创建了多视图的Leap2手姿势数据集 (ML2HP数据集) 来进行高级的手姿势识别. 这个数据集使用多个摄像头视图来捕捉人工智能应用的各种手动和属性.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人与计算机的交互

背景情况:

  • 准确的手姿势识别对于自然的人与计算机交互 (HCI) 至关重要.
  • 现有的数据集往往受到有限的视角和封闭的影响,阻碍了现实世界的应用开发.
  • 跳动运动控制器2提供精确的手跟踪功能.

研究的目的:

  • 介绍多视图的Leap2手姿势数据集 (ML2HP数据集),这是一个用于手姿势识别的新资源.
  • 提供一个全面的数据集,解决先前手动姿势数据集的局限性,特别是关于遮蔽和观点多样性的数据集.
  • 促进先进的人工智能驱动的HCI系统的开发.

主要方法:

  • 该ML2HP数据集是使用一个多视图记录设置与两个跳动控制器2设备被捕获的.
  • 该数据集包括来自21名受试者的714,000个实例,其中包括17种不同的手姿势.
  • 精确的手部属性,包括地标坐标,速度,方向和手指宽度,都被自动提取出来.

主要成果:

  • 数据集提供了一个平衡的对象,手的姿势和手的侧向 (左/右) 的表示.
  • 多视图方法有效地减轻了手的遮,使得连续跟踪和姿势估计成为可能.
  • 该数据集包含每例247个相关的手属性,为模型训练提供了丰富的信息.

结论:

  • ML2HP数据集是推动多式联动手姿势识别研究的宝贵资源.
  • 这一数据集将加速开发更强大,更准确的人机交互人工智能.
  • 数据集的全面性质支持创建复杂的HCI应用程序.