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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| Published in | IEEE transactions on information forensics and security Vol. 9; no. 6; pp. 988 - 998 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.06.2014
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | 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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| AbstractList | 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. |
| Author | Snoussi, Hichem Tian Wang |
| Author_xml | – sequence: 1 surname: Tian Wang fullname: Tian Wang email: wangtian8704@gmail.com organization: Inst. Charles Delaunay, Univ. of Technol. of Troyes, Troyes, France – sequence: 2 givenname: Hichem surname: Snoussi fullname: Snoussi, Hichem email: hichem.snoussi@utt.fr organization: Inst. Charles Delaunay, Univ. of Technol. of Troyes, Troyes, France |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram |
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