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Visual Tracking Based on Extreme Learning Machine and Sparse Representation
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning mac...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634458/ https://www.ncbi.nlm.nih.gov/pubmed/26506359 http://dx.doi.org/10.3390/s151026877 |
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author | Wang, Baoxian Tang, Linbo Yang, Jinglin Zhao, Baojun Wang, Shuigen |
author_facet | Wang, Baoxian Tang, Linbo Yang, Jinglin Zhao, Baojun Wang, Shuigen |
author_sort | Wang, Baoxian |
collection | PubMed |
description | The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. |
format | Online Article Text |
id | pubmed-4634458 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-46344582015-11-23 Visual Tracking Based on Extreme Learning Machine and Sparse Representation Wang, Baoxian Tang, Linbo Yang, Jinglin Zhao, Baojun Wang, Shuigen Sensors (Basel) Article The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. MDPI 2015-10-22 /pmc/articles/PMC4634458/ /pubmed/26506359 http://dx.doi.org/10.3390/s151026877 Text en © 2015 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/4.0/). |
spellingShingle | Article Wang, Baoxian Tang, Linbo Yang, Jinglin Zhao, Baojun Wang, Shuigen Visual Tracking Based on Extreme Learning Machine and Sparse Representation |
title | Visual Tracking Based on Extreme Learning Machine and Sparse Representation |
title_full | Visual Tracking Based on Extreme Learning Machine and Sparse Representation |
title_fullStr | Visual Tracking Based on Extreme Learning Machine and Sparse Representation |
title_full_unstemmed | Visual Tracking Based on Extreme Learning Machine and Sparse Representation |
title_short | Visual Tracking Based on Extreme Learning Machine and Sparse Representation |
title_sort | visual tracking based on extreme learning machine and sparse representation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634458/ https://www.ncbi.nlm.nih.gov/pubmed/26506359 http://dx.doi.org/10.3390/s151026877 |
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