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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Vydáno v:IEEE transactions on information forensics and security Ročník 9; číslo 6; s. 988 - 998
Hlavní autoři: Tian Wang, Snoussi, Hichem
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.06.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Popis
Shrnutí: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 optical flow orientation as a descriptor encoding the moving information of each video frame. The nonlinear one-class support vector machine classification algorithm, following a learning period characterizing the normal behavior of training frames, detects abnormal events in the current frame. Further, a fast version of the detection algorithm is designed by fusing the optical flow computation with a background subtraction step. We finally apply the method to detect abnormal events on several benchmark data sets, and show promising results.
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ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2014.2315971