Research on Human Behavior Recognition in Video Surveillance

摘要:

文章预览

For effectively solving human behavior recognition in video surveillance, a novel behavior recognition model is presented. New behaviors may be produced in the process of human motion, hierarchical Dirichlet process is used to cluster monitored feature data of human body to decide whether unknown behaviors occur or not. The infinite hidden Markov model is used to learn unknown behavior patterns with supervised method, and then update the knowledge base. When knowledge base reaches a certain scale, the system can analyze human behaviors with unsupervised method. The Viterbi decoding algorithm of HMM is adopted to analyze current behavior of the human motion. The simulation experiments show that this method has unique advantage over others.

信息:

期刊:

编辑:

Jingying Zhao

页数:

2276-2280

DOI:

10.4028/www.scientific.net/AMR.255-260.2276

引用:

W. Q. Zhu et al., "Research on Human Behavior Recognition in Video Surveillance", Advanced Materials Research, Vols. 255-260, pp. 2276-2280, 2011

上线时间:

May 2011

输出:

价格:

$35.00

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