基于相关点均值处理的Kinect人手位置检测方法研究

在人机交互领域中,人手的位置信息往往直接用于交互指令的解读与交互结果的计算,因此高精度的实时人手位置检测是实现非接触式的、自然的人机交互的重要基础.针对Kinect 2.0追踪人体骨骼点获取的三维坐标数据的波动和误差较大的问题,本文提出了基于相关点均值处理的人手位置检测算法.该算法基于深度信息,以手腕为分割阈值点,进行人手图像分割,并对人手位置信息相关点进行空间平均处理与时间平均处理,提高位置检测精度.实验结果表明:基于相关点均值处理的人手位置检测算法是有效的,检测误差在5 mm以内,能够满足在人机交互等应用系统中的基本要求.

Kinect human hand position detection based on mean processing of correlation points

In the field of human-computer interaction,the position information of human hands is often directly used in the interpretation of interactive instructions and the calculation of interactive results,therefore,high-precision real-time hand position detection is an important basis for non-contact and natural human-computer interaction.In order to solve the problem of fluctuation and error in the 3D coordinates acquired by Kinect 2.0,a hand position detection algorithm based on mean processing of correlation points is proposed in this paper.Based on the depth information,the hand image is segmented using the human wrist as the segmentation threshold,and the human hand position information is processed by spatial and temporal average of the related points to improve the accuracy of position detection.The experimental results show that the algorithm based on correlation points mean processing is effective and the detection error is less than 5 mm,which can meet the basic requirements of human-computer interaction.