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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-...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2013
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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. |
format | Online Article Text |
id | pubmed-3892866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
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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