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Sensors 2013, 13(12), 17130-17155; doi:10.3390/s131217130
Article

Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

1,* , 2
, 3
 and 1
Received: 30 October 2013; in revised form: 29 November 2013 / Accepted: 29 November 2013 / Published: 12 December 2013
(This article belongs to the Section Physical Sensors)
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Abstract: The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.
Keywords: abnormal detection; optical flow; covariance matrix descriptor; online least squares one-class SVM abnormal detection; optical flow; covariance matrix descriptor; online least squares one-class SVM
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Wang, T.; Chen, J.; Zhou, Y.; Snoussi, H. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection. Sensors 2013, 13, 17130-17155.

AMA Style

Wang T, Chen J, Zhou Y, Snoussi H. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection. Sensors. 2013; 13(12):17130-17155.

Chicago/Turabian Style

Wang, Tian; Chen, Jie; Zhou, Yi; Snoussi, Hichem. 2013. "Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection." Sensors 13, no. 12: 17130-17155.



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