基于来自单个自我中心深度图像的回归的手对象姿势估计
Jingang Lin1, Dongnian Li1, Chengjun Chen1
1School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究引入了一种回归方法,用于从深度图像中准确地估计手-物体姿势. 计算机视觉系统有效地解决了闭塞挑战,提高了人类互动理解.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
背景情况:
- 准确的手对象姿势估计对于理解人类互动至关重要.
- 严重的闭塞在基于视觉的姿势估计任务中是一个重大挑战.
- 现有的方法在混乱或封闭的场景中难以准确地确定姿势.
研究的目的:
- 开发一种强大的手-物体姿势估计方法,能够处理阻塞.
- 为了同时估计手和操纵对象的3D姿势.
- 提高计算机视觉系统在分析人与物体交互时的准确性和可靠性.
主要方法:
- 使用3D中心检测方法从深度图像中提取前景手对象信息.
- 在一个单一的框架内使用回归方法来同时估计姿势.
- 用ResNet-50骨干实现一个卷积神经网络 (CNN) 进行关键点预测.
主要成果:
- 拟议的方法在FPHA-HO数据集上实现了手的平均关键点误差11.85毫米,物体的平均误差18.97毫米.
- 回归技术有效地估计了尽管阻塞的姿势.
- 使用单个自我中心的深度图像证明了准确的姿势估计.
结论:
- 开发的回归方法提供了准确的手对象姿势估计,即使有显著的阻塞.
- 这一进步提高了计算机视觉系统的功能,用于详细分析人与物体的相互作用.
- 该方法显示了需要精确理解操纵任务的应用程序的潜力.
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