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

Sensors

Tian Wang¹  Jie Chen²  Yi Zhou³ 

[1] Institut Charles Delaunay-LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, Troyes 10004, France; E-Mail:;Observatoire de la Côte d'Azur-UMR 7293 CNRS, University of Nice Sophia-Antipolis, Nice 06108, France; E-Mail:;College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China; E-Mail:

关键词: abnormal detection;    optical flow;    covariance matrix descriptor;    online least squares one-class SVM;   

DOI  :  10.3390/s131217130

来源: mdpi

【 摘 要 】

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.

【 授权许可】

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