东南大学学报（自然科学版）2011，Vol.41Issue(3)：492-497,6.DOI:10.3969/j.issn.1001-0505.2011.03.012
视频监控中可变人体行为的识别
Recognition for changable human behaviors in video surveillance
摘要
Abstract
For effectively recognizing human behaviors in video surveillance, a novel behavior recognition model and a foreground extraction method are presented. For foreground detection, combining background edge model and background model, a foreground detection method is proposed, which can effectively avoid the light, shadows and other external factors. To quickly find new behaviors produced in the process of human motion, a hierarchical Dirichlet process is adopted to aggregate monitored feature data for human body to determine whether unknown behaviors are produced or not. The infinite hidden Markov model(HMM) is adopted to learn unknown behavior patterns with supervised method, and then update the knowledge base. When knowledge base reaches a certain scale, system can analyze human behaviors with unsupervised method. Simulation experiments show that the proposed method has unique advantage over others for human behavior detection in real-time video surveillance.关键词
视频监控/行为模式/行为识别/前景提取/多层Dirichlet过程Key words
video surveillance/ behavior pattern/ behavior recognition/ foreground extraction/ hierarchical Dirichlet process分类
信息技术与安全科学引用本文复制引用
满君丰,李倩倩,温向兵..视频监控中可变人体行为的识别[J].东南大学学报（自然科学版）,2011,41(3):492-497,6.基金项目
国家自然科学基金资助项目(60773110)、湖南省自然科学基金资助项目(09JJ6087)、中国包装总公司科研资助项目(2008-XK10)、湖南省科技计划资助项目(2010FJ3041)、湖南工业大学研究生创新基金资助项目(CX1003). （60773110）
