基于三维视觉特征的数字手势语义识别新方法研究

为了解决现有手势识别易受背景噪声干扰和算法较为复杂的问题,提出一种基于3D视觉的数字手势语义识别方法。首先,通过RealSense 3D相机采集手部区域的RGB图像和深度图像,并结合深度信息和肤色信息,对手势进行分割;其次,对手势图像进行形态学滤波后,得到手势区域的轮廓凸包面积比、凸缺陷数、手指夹角和关键点连线比值等特征参数;最后,通过分析不同手势独有的特征参数,实现准确的手势识别。对数字0～9的手势分别进行50次识别实验,手势分割准确率为100%,手势识别准确率为98.5%。实验表明该方法准确可靠,数字手势识别效果理想。

Research on a new method of digital gesture semantic recognition based on 3D visual features

In order to solve the existing problems that the hand gestures recognition is easily to be interfered by background noise and the algorithm is complex, a digital gesture semantic recognition method based on 3D vision is proposed. First of all, RGB and depth images of hand area were collected by Realsense 3D camera, and segmentation results of hand gesture were obtained by combining depth information and skin color information. Secondly, after morphological filtering of gesture images, the feature parameters of gesture region such as area ratio of contour to convex hull, number of convex defects, angle between fingers and the length ratio of key points connection were obtained. Finally, analyzed the unique characteristic parameters of different gestures to achieve accurate gesture recognition. The digital gesture recognition experiments of 0- 9 were carried out 50 times, the accuracy of gesture segmentation was 100%, and the accuracy of gesture recognition was 98. 5%. The experiments show that this method is accurate and reliable, and the effect of digital gesture recognition is ideal.