Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram

The aim of this paper is to detect abnormal events in video streams, a challenging but important subject in video surveillance. We propose a novel algorithm to address this problem. The algorithm is based on an image descriptor and a nonlinear classification method. We introduce a histogram of optic...

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Veröffentlicht in:	IEEE transactions on information forensics and security Jg. 9; H. 6; S. 988 - 998
Hauptverfasser:	Tian Wang, Snoussi, Hichem
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.06.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial intelligence Classification Computer science; control theory; systems Data processing. List processing. Character string processing Encoding Engineering Sciences Exact sciences and technology Feature extraction Forensic engineering Frames Histograms Memory organisation. Data processing Nonlinear optics Nonlinearity Optical imaging Orientation Pattern recognition. Digital image processing. Computational geometry Signal and Image processing Software Support vector machines Theoretical computing Training Vectors HOFO optical flow Abnormal detection one-class SVM Image subtraction Streaming Computer vision Histogram Descriptor system Image processing Image content Video signal Image segmentation Surveillance ABackground Scene analysis Vector support machine Anomaly Learning algorithm Artificial intelligence Optical flow Binary classification
ISSN:	1556-6013, 1556-6021
Online-Zugang:	Volltext
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