基于Kinect骨骼信息与深度图像的指尖点检测

Fingertips recognition based on Kinect skeleton information and depth data.

摘要:

针对普通摄像头手势识别系统易受复杂环境和光照条件等因素影响，存在对指尖点的漏判、误判问题，提出一种基于Kinect 骨骼信息与深度图像的掌心点提取和指尖点检测的手势识别方法。在DRVI平台上创建Kinect的接口控件，对Kinect传感器获取人体骨骼信息和深度图像进行分析，采用了坐标映射、图像分割、距离变换的关键技术和方法从深度图中分割出手势部分区域，对手势区域形态学处理，结合凸包和K-曲率算法检测不同手势中指尖点的个数和位置，计算不同手势凸包轮廓上的点集生成的HOG(Histogram of Oriented Gradient)特征描述子，最后利用特征描述子对预定的6种数字手势进行识别。经实验测试可以在复杂环境和不同光照情况下正确识别指尖点。

Abstract:

The hand gesture recognition system is susceptible to the light conditions and complex environments by using ordinary camera, so the fingertips are always missed and misjudged. To these problems, a new method for fingertip detection and palm point extraction of hand gesture is proposed based on Kinect skeleton information and depth image. Creating a Kinect control on the DRVI platform and Analyzing the information of the skeleton and depth image is acquired by Kinect sensor, the technology and method of coordinate mapping ,image segmentation and distance conversion can use to segment the hand area on the depth image, which need morphological processing .After the number of fingertips and the location for hand gesture being detection with combining convex hull and K-curvature algorithm, calculating the HOG feature descriptor generated by the point set on the contours of different gesture hulls.Finally,the HOG feature descriptor is applied to identify six scheduled number hand gesture. The experiment results show that the proposed method can identify fingertips in complex environments and different lighting conditions.