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Sequential Monte Carlo-guided ensemble tracking
A great deal of robustness is allowed when visual tracking is considered as a classification problem. This paper combines a finite number of weak classifiers in a SMC framework as a strong classifier. The time-varying ensemble parameters (confidence of weak classifiers) are regarded as sequential ar...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5388463/ https://www.ncbi.nlm.nih.gov/pubmed/28399149 http://dx.doi.org/10.1371/journal.pone.0173297 |
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author | Wang, Yuru Liu, Qiaoyuan Jiang, Longkui Yin, Minghao Wang, Shengsheng |
author_facet | Wang, Yuru Liu, Qiaoyuan Jiang, Longkui Yin, Minghao Wang, Shengsheng |
author_sort | Wang, Yuru |
collection | PubMed |
description | A great deal of robustness is allowed when visual tracking is considered as a classification problem. This paper combines a finite number of weak classifiers in a SMC framework as a strong classifier. The time-varying ensemble parameters (confidence of weak classifiers) are regarded as sequential arriving states and their posterior distribution is estimated in a Bayesian manner. Therefore, both the adaptiveness and stability are kept for the ensemble classification in handling scene changes and target deformation. Moreover, to increase the tracking accuracy, weak classifiers including Support Vector Machine (SVM) and Large Margin Distribution Machine (LDM) are combined as a hybrid strong one, with adaptiveness to the sample scales. Comprehensive experiments are performed on benchmark videos with various tracking challenges, and the proposed method is demonstrated to be better than or comparable to the state-of-the-art trackers. |
format | Online Article Text |
id | pubmed-5388463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-53884632017-05-03 Sequential Monte Carlo-guided ensemble tracking Wang, Yuru Liu, Qiaoyuan Jiang, Longkui Yin, Minghao Wang, Shengsheng PLoS One Research Article A great deal of robustness is allowed when visual tracking is considered as a classification problem. This paper combines a finite number of weak classifiers in a SMC framework as a strong classifier. The time-varying ensemble parameters (confidence of weak classifiers) are regarded as sequential arriving states and their posterior distribution is estimated in a Bayesian manner. Therefore, both the adaptiveness and stability are kept for the ensemble classification in handling scene changes and target deformation. Moreover, to increase the tracking accuracy, weak classifiers including Support Vector Machine (SVM) and Large Margin Distribution Machine (LDM) are combined as a hybrid strong one, with adaptiveness to the sample scales. Comprehensive experiments are performed on benchmark videos with various tracking challenges, and the proposed method is demonstrated to be better than or comparable to the state-of-the-art trackers. Public Library of Science 2017-04-11 /pmc/articles/PMC5388463/ /pubmed/28399149 http://dx.doi.org/10.1371/journal.pone.0173297 Text en © 2017 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, Yuru Liu, Qiaoyuan Jiang, Longkui Yin, Minghao Wang, Shengsheng Sequential Monte Carlo-guided ensemble tracking |
title | Sequential Monte Carlo-guided ensemble tracking |
title_full | Sequential Monte Carlo-guided ensemble tracking |
title_fullStr | Sequential Monte Carlo-guided ensemble tracking |
title_full_unstemmed | Sequential Monte Carlo-guided ensemble tracking |
title_short | Sequential Monte Carlo-guided ensemble tracking |
title_sort | sequential monte carlo-guided ensemble tracking |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5388463/ https://www.ncbi.nlm.nih.gov/pubmed/28399149 http://dx.doi.org/10.1371/journal.pone.0173297 |
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