HMMs-Based Human Action Recognition for an Intelligent Household Surveillance Robot

摘要：

The aging of population has become a social problem and fall is a major health risk in the elderly. To this end, this paper presents a novel approach for fall detection applied to an intelligent household surveillance robot. Silhouette based features are extracted, including aspect ratio of minimal bounding box of the human silhouette, approximated elliptical eccentricity, normalized central moments and Hu moments. Fall and other human motions, such as walk, bend, run and crouch, are modeled using Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM). The experimental results are evaluated by sensitivity, specificity and accuracy and the average of them reaches 88.71％, 97.56％ and 96.26％ respectively.

作者: Qiaoyun Zhou Shiqi Yu Xinyu Wu Qiao Gao Chongguo Li Yangsheng Xu

作者单位: Shenzhen Institute of Advanced Integration Technology Shenzhen Institute of Advanced Technology The Chinese University of Hongkong

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

会议地点: 桂林

会议语种:英文

页码: 2295-2300

在线出版日期: 2009-12-19（万方平台首次上网日期，不代表论文的发表时间）