提出了基于kInECT传感器深度信息的动态手势识别方法,在预处理阶段通过OPEnCV快速跟踪手部,有效分割手势.为改进动态手势轨迹的提取和分类,引入隐马尔可夫模型(HMM)对手势轨迹进行训练和识别.实验结果表明,基于HMM的识别方法对具有时空特性的动态手势有很高的识别率,在不同光照和复杂背景下有鲁棒性的结果.Based on the depth data obtained from a Microsoft's Kinect sensor,this paper proposed a reliable method for dynamic gesturerecognition.We preprocessed the raw depth datato quickly trackthe hand palm and segment the hand gesture by using OpenCVlibrary.To improve the extraction and classification of the dynamic gesturetrajectory,we introduced the Hidden Markov model for training and recognition.The experimental results show that HMM-based method has high recognition rateand strong robustness in the different illuminationconditions and complex backgrounds.国防基础科研计划项目; 国防科研重点实验室基
