自动化学报2017，Vol.43Issue(3)：398-406,9.DOI:10.16383/j.aas.2017.c160130
基于隐马尔科夫模型的人体动作压缩红外分类
Compressive Infrared Classification of Human Motion Using HMM
摘要
Abstract
Infrared radiation changes (IRC) induced by human motion can provide important clue for motion classifica-tion. This paper presents a hidden Markov model (HMM)-based compressive infrared classification method to recognize human motions. In order to solve the problem of self-occlusion, an orthogonal-view based compressive infrared sensing system is implemented for pro jecting the IRC to two orthogonal planes in the infrared radiation field. Then, a double-layer feature model using HMM classifier is trained to carry out motion recognition with the compressive measurements. Experimental results show that the mean correct classification rate with double-layer feature is 95.71%, which is better than that with main-layer feature. This method provides a new approach to classification of human motions for ambient assisted system.关键词
环境辅助生活/隐马尔科夫模型/压缩感知/热释电红外传感器/动作分类Key words
Ambient assisted living (AAL)/hidden Markov model (HMM)/compressive sensing/pyroelectric infrared sensors/motion classification引用本文复制引用
关秋菊,罗晓牧,郭雪梅,王国利..基于隐马尔科夫模型的人体动作压缩红外分类[J].自动化学报,2017,43(3):398-406,9.基金项目
国家自然科学基金(61375080, 61301294, 61601523), 广东省自然科学基金(2015A030311049, 2016A030310238), 广东省教育厅青年创新人才项目(2015KQNCX068) 资助Supported by National Natural Science Foundation of China (61375080, 61301294, 61601523), Natural Science Foundation of Guangdong Province (2015A030311049, 2016A030310238), and Young Creative Talents Project of Guangdong Province Educa-tion Department (2015KQNCX068) （61375080, 61301294, 61601523）
