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Keywords = online least squares one-class SVM

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26 pages, 1258 KiB  
Article
Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
by Tian Wang, Jie Chen, Yi Zhou and Hichem Snoussi
Sensors 2013, 13(12), 17130-17155; https://doi.org/10.3390/s131217130 - 12 Dec 2013
Cited by 31 | Viewed by 8160
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 [...] Read more.
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. Full article
(This article belongs to the Section Physical Sensors)
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