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

• Published in: Sensors (Basel, Switzerland) Vol. 13; no. 12; pp. 17130 - 17155
• Main Authors: Wang, Tian, Chen, Jie, Zhou, Yi, Snoussi, Hichem
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.12.2013; Molecular Diversity Preservation International (MDPI)
• Subjects: abnormal detection; Algorithms; covariance matrix descriptor; Datasets; Least-Squares Analysis; Methods; online least squares one-class SVM; optical flow; Sensors; Support Vector Machine; Support vector machines; Surveillance; France
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s131217130

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.