Online least squares one-class support vector machines-based abnormal visual event detection

14Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

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. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Cite

CITATION STYLE

APA

Wang, T., Chen, J., Zhou, Y., & Snoussi, H. (2013). Online least squares one-class support vector machines-based abnormal visual event detection. Sensors (Switzerland), 13(12), 17130–17155. https://doi.org/10.3390/s131217130

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free