title: Online least squares one-class support vector machines-based abnormal visual event detection.
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.
authors.affiliation: Institut Charles Delaunay-LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, Troyes 10004, France. wangtian8704@gmail.com.
authors.forename: Tian
authors.initials: T
authors.lastname: Wang
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhou
authors.forename: Hichem
authors.initials: H
authors.lastname: Snoussi
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D060388:Support Vector Machine