Abnormal activity detection for surveillance video synopsis

查看全文 作　　者：[1]祝晓斌;Wang [1]Qian;Li [1]Haisheng;Guo [2]Xiaoxia;Xi [2]Yan;Shen [2]Yang

机构地区：[1]School of Computer and Information Engineering,Beijing Technology and Business University;[2]Academy of Broadcasting Science,SAPPRFT

出　　处：《High Technology Letters》索引2016年第22卷第2期,共7页

基　　金：Supported by the National Natural Science Foundation of China(No.61402023);Beijing Technology and Business' University Youth Fund(No.QNJJ2014-23);Beijing Natural Science Foundation(No.4162019) 摘　　要：Video synopsis is an effective and innovative way to produce short video abstraction for huge video archives,while keeping the dynamic characteristic of activities in the original video.Abnormal activity,as the critical event,is always the main concern in video surveillance context.However,in traditional video synopsis,all the normal and abnormal activities are condensed together equally,which can make the synopsis video confused and worthless.In addition,the traditional video synopsis methods always neglect redundancy in the content domain.To solve the above-mentioned issues,a novel video synopsis method is proposed based on abnormal activity detection and key observation selection.In the proposed algorithm,activities are classified into normal and abnormal ones based on the sparse reconstruction cost from an atomically learned activity dictionary.And key observation selection using the minimum description length principle is conducted for eliminating content redundancy in normal activity.Experiments conducted in publicly available datasets demonstrate that the proposed approach can effectively generate satisfying synopsis videos. 关键词：视频摘要视频监控异常检测最小描述长度动态特性重构成本学习活动

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Abnormal activity detection for surveillance video synopsis

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