借鉴人类视觉感知所具有的多尺度、多分辨性的特性,针对智能视频监控系统的人体运动行为识别,提出了一种基于多尺度特征的双层隐马尔可夫模型.根据人体行为关键姿态数确定HMM的状态数目,发掘人体运动行为隐藏的多尺度结构间的关系,将运动轨迹和人体姿态边缘小波矩2个不同尺度特征应用于2层HMM,提供更为丰富的行为尺度间的相关信息.分别用Weizmann人体行为数据库和自行拍摄的室内视频,对人体运动行为识别进行仿真实验,结果表明,五状态HMM模型更符合人体运动行为特点,基于多尺度特征的五状态双层隐马尔可夫模型具有较高的识别率.%Learning from multi-scale and multi-distinguish attributes of human beings' visual perception and aiming at human movement behavior recognition in intelligent video surveillance system, a double-layer hidden markov model ( DL-HMM) is developed based on multi-scale behavior features. Considering the human behavior characteristics , the number of HMM states is according to the number of key gestures selected. Discovering the relationship between the multi-scale structures hidden in the human movement behavior, two different scale features-human motion trajectory and wavelet moment of human gesture' s edge, are applied respectively in two layers of DL-HMM, so as to provide more scale information about behavior. Experiments, using Israel Weizmann human behavior database and human actions indoor recorded by ourselves, show the five-state HMM more accords with the human motion behavior characteristics, and the five-state DL-HMM based on multi-scale feature has a higher recognition rate compared with traditional methods using one layer HMM.
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