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

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-...

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Detalles Bibliográficos
Autores principales: Wang, Tian, Chen, Jie, Zhou, Yi, Snoussi, Hichem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892866/
https://www.ncbi.nlm.nih.gov/pubmed/24351629
http://dx.doi.org/10.3390/s131217130
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author Wang, Tian
Chen, Jie
Zhou, Yi
Snoussi, Hichem
author_facet Wang, Tian
Chen, Jie
Zhou, Yi
Snoussi, Hichem
author_sort Wang, Tian
collection PubMed
description 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.
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spelling pubmed-38928662014-01-16 Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection Wang, Tian Chen, Jie Zhou, Yi Snoussi, Hichem Sensors (Basel) Article 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. Molecular Diversity Preservation International (MDPI) 2013-12-12 /pmc/articles/PMC3892866/ /pubmed/24351629 http://dx.doi.org/10.3390/s131217130 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Wang, Tian
Chen, Jie
Zhou, Yi
Snoussi, Hichem
Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
title Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
title_full Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
title_fullStr Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
title_full_unstemmed Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
title_short Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
title_sort online least squares one-class support vector machines-based abnormal visual event detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892866/
https://www.ncbi.nlm.nih.gov/pubmed/24351629
http://dx.doi.org/10.3390/s131217130
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