收稿日期: 2012-04-13
修回日期: 2012-05-17
网络出版日期: 2012-05-17
基金资助
国家自然科学基金(61071173)资助
Abnormal behavior analysis based on latent topic model
Received date: 2012-04-13
Revised date: 2012-05-17
Online published: 2012-05-17
赵龙 , 郭立 , 谢锦生 , 刘皓 , 陆海先 . 基于隐含主题模型的异常行为分析[J]. 中国科学院大学学报, 2013 , 30(3) : 387 -393 . DOI: 10.7523/j.issn.1002-1175.2013.03.017
Considering that most of abnormal behavior analysis methods do not consider the scene, we propose a method of abnormal behavior analysis based on latent topic model. Features of the scene are extracted and clustered to visual vocabulary by K-means. The visual vocabulary is divided into semantic topics to describe the scene by pLSA model. The descriptions for the scene and trajectory are combined to form feature vector which is modeled by CRF. Parameters of the CRF model are estimated by training, and abnormal behavior is analyzed by inference. The experiments show that abnormal behavior in a particular scene is accurately analyzed by this method.
Key words： latent topic model; abnormal behavior analysis; pLSA; CRF; global behavior
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