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 Institut Charles Delaunay-LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, Troyes 10004, France 2 Observatoire de la Côte d'Azur-UMR 7293 CNRS, University of Nice Sophia-Antipolis, Nice 06108, France 3 College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
* Author to whom correspondence should be addressed.
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

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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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