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Multi-View Structural Local Subspace Tracking
In this paper, we propose a multi-view structural local subspace tracking algorithm based on sparse representation. We approximate the optimal state from three views: (1) the template view; (2) the PCA (principal component analysis) basis view; and (3) the target candidate view. Then we propose a un...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419779/ https://www.ncbi.nlm.nih.gov/pubmed/28333088 http://dx.doi.org/10.3390/s17040666 |
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author | Guo, Jie Xu, Tingfa Shi, Guokai Rao, Zhitao Li, Xiangmin |
author_facet | Guo, Jie Xu, Tingfa Shi, Guokai Rao, Zhitao Li, Xiangmin |
author_sort | Guo, Jie |
collection | PubMed |
description | In this paper, we propose a multi-view structural local subspace tracking algorithm based on sparse representation. We approximate the optimal state from three views: (1) the template view; (2) the PCA (principal component analysis) basis view; and (3) the target candidate view. Then we propose a unified objective function to integrate these three view problems together. The proposed model not only exploits the intrinsic relationship among target candidates and their local patches, but also takes advantages of both sparse representation and incremental subspace learning. The optimization problem can be well solved by the customized APG (accelerated proximal gradient) methods together with an iteration manner. Then, we propose an alignment-weighting average method to obtain the optimal state of the target. Furthermore, an occlusion detection strategy is proposed to accurately update the model. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms the state-of-the-art trackers in a wide range of tracking scenarios. |
format | Online Article Text |
id | pubmed-5419779 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54197792017-05-12 Multi-View Structural Local Subspace Tracking Guo, Jie Xu, Tingfa Shi, Guokai Rao, Zhitao Li, Xiangmin Sensors (Basel) Article In this paper, we propose a multi-view structural local subspace tracking algorithm based on sparse representation. We approximate the optimal state from three views: (1) the template view; (2) the PCA (principal component analysis) basis view; and (3) the target candidate view. Then we propose a unified objective function to integrate these three view problems together. The proposed model not only exploits the intrinsic relationship among target candidates and their local patches, but also takes advantages of both sparse representation and incremental subspace learning. The optimization problem can be well solved by the customized APG (accelerated proximal gradient) methods together with an iteration manner. Then, we propose an alignment-weighting average method to obtain the optimal state of the target. Furthermore, an occlusion detection strategy is proposed to accurately update the model. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms the state-of-the-art trackers in a wide range of tracking scenarios. MDPI 2017-03-23 /pmc/articles/PMC5419779/ /pubmed/28333088 http://dx.doi.org/10.3390/s17040666 Text en © 2017 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guo, Jie Xu, Tingfa Shi, Guokai Rao, Zhitao Li, Xiangmin Multi-View Structural Local Subspace Tracking |
title | Multi-View Structural Local Subspace Tracking |
title_full | Multi-View Structural Local Subspace Tracking |
title_fullStr | Multi-View Structural Local Subspace Tracking |
title_full_unstemmed | Multi-View Structural Local Subspace Tracking |
title_short | Multi-View Structural Local Subspace Tracking |
title_sort | multi-view structural local subspace tracking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419779/ https://www.ncbi.nlm.nih.gov/pubmed/28333088 http://dx.doi.org/10.3390/s17040666 |
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