基于隐马尔科夫模型的人体动作压缩红外分类

人体动作产生的辐射能量变化（Infrared radiation changes，IRC）信号是动作识别的重要线索，本文提出了一种基于隐马尔科夫模型的人体动作压缩红外分类新方法.针对人体动作的自遮挡问题，建立基于正交视角的压缩红外测量系统，获取人体动作在主投影面和辅助投影面的IRC压缩信号；然后，采用隐马尔科夫模型（Hidden Markov model，HMM）双层特征建模算法进行压缩域动作分类.实验结果表明双层特征建模的平均正确分类率高于主层特征建模，平均正确分类率可达95.71%.该方法为环境辅助生活系统提供了人体动作识别的新途径.

Compressive Infrared Classification of Human Motion Using HMM

Infrared radiation changes (IRC) induced by human motion can provide important clue for motion classification. 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 projecting 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.