中文摘要：

首次将连续型隐马尔科夫模型应用于静态手势识别中,根据该方法的特点,选用手型轮廓像素点坐标值序列作为静态手势的数据特征.采用微软公司的Kinect体感设备提取并追踪手势,为几种常用的静态手势训练HMM模型库,并使用该模型库进行静态手势识别实验.实验将该方法与使用SVM方法进行对比,结果表明这种方法的识别率高,训练模型所需样本少,简单灵活.

英文摘要：

In the paper, the continuous hidden Markov model is applied in static gesture recognition for the first time. According to the characteristics of CHMM, the pixel coordinates sequence of hand contour is chosen as a learning sample. It extracts and tracks the hand gesture by Kinect, and trains CHMM model library which is used for static gesture recognition. At last, the method is compared with the SVM method in an experiment. Experimental result shows the method is efficient, flexible and need minority samples.