Optimization Method of Human Posture Recognition Based on Kinect V2 Sensor

Hang Li

doi:10.3390/biomimetics10040254

2025-04-21

DOI：10.3390/biomimetics10040254

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人体动作识别旨在理解人类行为，对于提升人机交互和仿生机器人的智能性与自然性至关重要。本文提出一种结合Kinect传感器与OpenPose算法、Levenberg-Marquardt（LM）算法和Dynamic Time Warping（DTW）算法的方法，以改善动作识别的复杂度和实时性能。首先，利用Kinect V2深度传感器采集人体彩色图像、深度图像及三维骨骼点信息。接着，使用OpenPose处理彩色图像以提取二维骨骼点信息，并将其映射至深度图像以获取三维骨骼点信息。随后，采用LM算法融合三维骨骼点序列与Kinect获取的序列，生成稳定的二维骨骼点序列。最后，利用DTW算法识别复杂动作。不同场景和动作的实验结果表明，该方法稳定且准确，平均识别率达到95.94%。该方法有效解决了Kinect在收集骨骼点时出现的抖动和自遮挡等问题。该方法的鲁棒性和准确性使其非常适合应用于机器人交互系统中。

Keyword:

human-computer interaction

human action recognition

bone point optimization

期刊

Biomimetics

IF:

3.9

论文数:

3.2K

被引数:

5.1K

机构

Xian Univ

学者数:

论文数: 23

被引数: 11

Xi'an Institute of Optics and Precision Mechanics

学者数:

论文数: 14

被引数: 0

xian longviews elect engn co ltd

学者数:

论文数: 1

被引数: 0

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