%0 Journal Article
%T Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
%A Tian Wang
%A Jie Chen
%A Yi Zhou
%A Hichem Snoussi
%J Sensors
%D 2013
%I MDPI AG
%R 10.3390/s131217130
%X 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.
%K abnormal detection
%K optical flow
%K covariance matrix descriptor
%K online least squares one-class SVM
%U http://www.mdpi.com/1424-8220/13/12/17130