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基于Kinect V2传感器的人体姿势识别的优化方法

Hang Li1,2, Hao Li1, Ying Qin3

  • 1School of Information Engineering, Xi'an University, Xi'an 710065, China.

Biomimetics (Basel, Switzerland)
|April 25, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种使用Kinect,OpenPose,Levenberg-Marquardt (LM) 和动态时间扭曲 (DTW) 算法的新型人类行动识别方法. 该方法实现了95.94%的准确性,增强了人机交互和机器人技术.

关键词:
骨点优化优化 骨点优化人类行动承认承认人与计算机的互动.

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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:

  • 人类行为识别对于智能系统至关重要.
  • 现有的方法面临着数据复杂性和实时处理的挑战.
  • Kinect 传感器数据可能容易发生动和自我封闭.

研究的目的:

  • 为了提高人类行动识别的复杂性和实时性能.
  • 开发一种强大的方法来识别各种场景中的人类行为.
  • 为了提高骨数据采集的稳定性和准确性.

主要方法:

  • 结合Kinect V2传感器与OpenPose,Levenberg-Marquardt (LM) 和动态时间扭曲 (DTW) 算法.
  • 使用OpenPose提取2D骨数据,将其映射到3D骨数据的深度图像中.
  • 使用LM融合Kinect和OpenPose的3D骨架数据,然后使用DTW识别动作.

主要成果:

  • 在各种场景和行动中实现了95.94%的平均动作识别率.
  • 在Kinect骨架数据中成功解决了动和自我封闭等问题.
  • 在复杂的人类运动识别中表现出稳定性和准确性.

结论:

  • 拟议的方法为人类行动识别提供了稳定而准确的解决方案.
  • 与单独使用Kinect相比,它显著提高了骨数据的质量.
  • 这种方法非常适合在机器人交互系统中的现实应用.