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Received:October 30, 2015 Revised:January 11, 2016
Received:October 30, 2015 Revised:January 11, 2016
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.
Abstract: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.
keywords: static gesture recognition continuous Hidden Markov Model Kinect the pixel coordinates sequence of and contour
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基金项目:国家高技术研究发展计划（863）（2012aa01a509，2012aa01a510）；国家自然科学基金（61101202）
引用文本：
吴彩芳,谢钧,俞璐,周开店.连续隐马尔科夫的静态手势识别法.计算机系统应用,2016,25(8):115-119
WU Cai-Fang,XIE Jun,YU Lu,ZHOU Kai-Dian.Static Gesture Recognition Arithmetic Based on CHMM.COMPUTER SYSTEMS APPLICATIONS,2016,25(8):115-119
吴彩芳,谢钧,俞璐,周开店.连续隐马尔科夫的静态手势识别法.计算机系统应用,2016,25(8):115-119
WU Cai-Fang,XIE Jun,YU Lu,ZHOU Kai-Dian.Static Gesture Recognition Arithmetic Based on CHMM.COMPUTER SYSTEMS APPLICATIONS,2016,25(8):115-119
