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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...
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/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. |
format | Online Article Text |
id | pubmed-6163504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT xuexizhe unmannedaerialvehicleobjecttrackingbycorrelationfilterwithadaptiveappearancemodel AT liying unmannedaerialvehicleobjecttrackingbycorrelationfilterwithadaptiveappearancemodel AT shenqiang unmannedaerialvehicleobjecttrackingbycorrelationfilterwithadaptiveappearancemodel |