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Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning
Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209897/ https://www.ncbi.nlm.nih.gov/pubmed/30340356 http://dx.doi.org/10.3390/s18103513 |
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author | Yoon, Gang-Joon Hwang, Hyeong Jae Yoon, Sang Min |
author_facet | Yoon, Gang-Joon Hwang, Hyeong Jae Yoon, Sang Min |
author_sort | Yoon, Gang-Joon |
collection | PubMed |
description | Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional descriptors using the standard sparse representation approach. Therefore, this study proposes a structured sparse principal component analysis to represent the complex appearance descriptors of the target object effectively with a linear combination of a small number of elementary atoms chosen from an over-complete dictionary. Using an online dictionary for learning and updating by selecting similar dictionaries that have high probability makes it possible to track the target object in a variety of environments. Qualitative and quantitative experimental results, including comparison to the current state of the art visual object tracking algorithms, validate that the proposed tracking algorithm performs favorably with changes in the target object and environment for benchmark video sequences. |
format | Online Article Text |
id | pubmed-6209897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62098972018-11-02 Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning Yoon, Gang-Joon Hwang, Hyeong Jae Yoon, Sang Min Sensors (Basel) Article Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional descriptors using the standard sparse representation approach. Therefore, this study proposes a structured sparse principal component analysis to represent the complex appearance descriptors of the target object effectively with a linear combination of a small number of elementary atoms chosen from an over-complete dictionary. Using an online dictionary for learning and updating by selecting similar dictionaries that have high probability makes it possible to track the target object in a variety of environments. Qualitative and quantitative experimental results, including comparison to the current state of the art visual object tracking algorithms, validate that the proposed tracking algorithm performs favorably with changes in the target object and environment for benchmark video sequences. MDPI 2018-10-18 /pmc/articles/PMC6209897/ /pubmed/30340356 http://dx.doi.org/10.3390/s18103513 Text en © 2018 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 Yoon, Gang-Joon Hwang, Hyeong Jae Yoon, Sang Min Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning |
title | Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning |
title_full | Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning |
title_fullStr | Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning |
title_full_unstemmed | Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning |
title_short | Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning |
title_sort | visual object tracking using structured sparse pca-based appearance representation and online learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209897/ https://www.ncbi.nlm.nih.gov/pubmed/30340356 http://dx.doi.org/10.3390/s18103513 |
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