Real-time Multiple Abnormality Detection in Video Data

Publikation: Forskning - peer reviewKonferenceartikel i proceeding

Abstrakt

Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.
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Detaljer

Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.
OriginalsprogEngelsk
TitelINSTICC : The International Conference on Computer Vision Theory and Applications
Antal sider6
UdgiverInstitute for Systems and Technologies of Information, Control and Communication
Publikationsdato2013
Artikelnummer140
StatusUdgivet - 2013
Begivenhed8th International Conference on Computer Vision Theory and Applications - Barcelona, Spanien

Konference

Konference8th International Conference on Computer Vision Theory and Applications
Nummer8
LandSpanien
ByBarcelona
Periode21/02/201324/02/2013
Internetadresse

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