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Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model

With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. Howeve...

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Detalles Bibliográficos
Autores principales: Xue, Xizhe, Li, Ying, Shen, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163504/
https://www.ncbi.nlm.nih.gov/pubmed/30134621
http://dx.doi.org/10.3390/s18092751
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author Xue, Xizhe
Li, Ying
Shen, Qiang
author_facet Xue, Xizhe
Li, Ying
Shen, Qiang
author_sort Xue, Xizhe
collection PubMed
description With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach.
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spelling pubmed-61635042018-10-10 Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model Xue, Xizhe Li, Ying Shen, Qiang Sensors (Basel) Article With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach. MDPI 2018-08-21 /pmc/articles/PMC6163504/ /pubmed/30134621 http://dx.doi.org/10.3390/s18092751 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
Xue, Xizhe
Li, Ying
Shen, Qiang
Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model
title Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model
title_full Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model
title_fullStr Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model
title_full_unstemmed Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model
title_short Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model
title_sort unmanned aerial vehicle object tracking by correlation filter with adaptive appearance model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163504/
https://www.ncbi.nlm.nih.gov/pubmed/30134621
http://dx.doi.org/10.3390/s18092751
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