基于深度相机的腕部识别与手掌中心估计

Zhengwei Yao, Zhigeng Pan, Shuchang Xu
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引用次数: 12

摘要

当手和前臂进入深度相机的可用深度范围时,将手和前臂的数据一起提取。如果将这些数据作为一个整体来处理,可能会影响到一些重要的算法,如手掌中心估计、手部方向估计和手部跟踪。分析了腕部的运动特征和手的轮廓特征,利用内切矩形的几何特征,提出了一种腕部识别算法。为了减少掌心估计的计算时间,分析了锐角三角形和内切圆的几何特征,结合手交互的特点,提出了一种新的掌心估计算法。实验证明,手腕识别算法可以很好地将手和前臂分开,并且新的手掌中心估计算法在性能上比原有算法有明显的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wrist Recognition and the Center of the Palm Estimation Based on Depth Camera
When the hand and the forearm enter the available depth range of the depth camera, the data of the hand and the forearm will be extracted together. If process these data as a whole, which maybe affect some important algorithms such as the center of the palm estimation, the orientation of the hand estimation and hand tracing. The paper analyzes the motion features of the wrist and the contour features of the hand, takes advantage of the geometric characteristics of an inscribed rectangle and proposes a wrist recognition algorithm. In order to reduce the computing time of estimating the center of the palm, the paper analyzes the geometric characteristics of an acute triangle and an inscribed circle, combines the features of the hand interaction and proposes a new algorithm of estimating the center of the palm. Proved by the experiments, the wrist recognition algorithm can separate the hand from the forearm well, and the new algorithm of estimating the center of the palm has a distinct advantage over the original algorithms in the performance.
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