基于深度相机的手腕识别与掌心估测

The wrist recognition and the center of palm estimation based on depth camera

目的当手和手臂都进入深度相机所设定的有效深度范围时,它们将被作为一个整体来提取,若处理时也把它们作为一个整体,这可能会影响手势交互的一些重要算法,如掌心估测、手朝向估测、手的跟踪等。掌心是手势交互中较为稳定的点,掌心与手簇中心的连线常被用来估测手的朝向。因此提高掌心估测算法的性能有助于提高手势交互的整体性能。方法为了有效地分割手与手臂,从分析手腕的运动特征和手的轮廓特点入手,并利用内切矩形的几何特征,提出手腕识别算法;为了提高掌心估测的性能,从手势交互的特点入手,分析了锐角三角形和最大内切圆的几何特征,提出新的掌心估测算法。结果本文算法在空气多点触摸系统中进行了实验,新的掌心估测算法较之原算法在性能上提高了近7倍,且仍然能保持掌心坐标的稳定性,坐标偏差不大于3个像素。同时手腕识别算法的引入也提高了掌心估测的准确性。结论实验结果表明,手腕识别算法能较好地分割出手与手臂,新的掌心估测算法能很好地支持实时交互。

Objective 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. Processing these data as a whole may affect some important algorithms such as the center of palm estimation, the orientation of hand estimation and hand tracing. The center of the palm is quite stable in the gesture interaction, so the line through the center of the palm and the center of the hand cluster is usual used as a hand orientation indicator. As a result, improving the performance of the center of palm estimation is favorable for increasing the overall performance. Method In order to correctly separate the hand from the forearm, the research begins with finding the motion features of the wrist and the contour features of the hand, and takes advantage of the geometric characteristics of an inscribed rectangle. At last a wrist recognition algorithm has been proposed. For improving the performance of the center of palm estimation, we start with analyzing the geometric characteristics of an acute triangle and an inscribed circle, and combine the features of the hand interaction. Finally a new algorithm of estimating the center of palm is proposed. Result The algorithms above are tested in an air multi-touch system. The proposed algorithm in this paper runs nearly 7 times quic- ker than the original algorithm, and still can keep the stability of the estimated coordinate of the center of palm, coordinate deviation not more than 3 pixels. Moreover, the success rate of the center of palm estimation is improved by using the wrist recognition algorithm. The accuracy of the center of palm estimation is improved due to using the wrist recognition algo-rithm. Conclusion Our experiments proved that the wrist recognition algorithm ean separate the hand from the forearm well, amt the new algorithm of the center of palm estimation can support real time interaction well.