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Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detectio...

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Autores principales: Ma, Yalong, Wu, Xinkai, Yu, Guizhen, Xu, Yongzheng, Wang, Yunpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850960/
https://www.ncbi.nlm.nih.gov/pubmed/27023564
http://dx.doi.org/10.3390/s16040446
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author Ma, Yalong
Wu, Xinkai
Yu, Guizhen
Xu, Yongzheng
Wang, Yunpeng
author_facet Ma, Yalong
Wu, Xinkai
Yu, Guizhen
Xu, Yongzheng
Wang, Yunpeng
author_sort Ma, Yalong
collection PubMed
description Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.
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spelling pubmed-48509602016-05-04 Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery Ma, Yalong Wu, Xinkai Yu, Guizhen Xu, Yongzheng Wang, Yunpeng Sensors (Basel) Article Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness. MDPI 2016-03-26 /pmc/articles/PMC4850960/ /pubmed/27023564 http://dx.doi.org/10.3390/s16040446 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ma, Yalong
Wu, Xinkai
Yu, Guizhen
Xu, Yongzheng
Wang, Yunpeng
Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery
title Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery
title_full Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery
title_fullStr Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery
title_full_unstemmed Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery
title_short Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery
title_sort pedestrian detection and tracking from low-resolution unmanned aerial vehicle thermal imagery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850960/
https://www.ncbi.nlm.nih.gov/pubmed/27023564
http://dx.doi.org/10.3390/s16040446
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AT yuguizhen pedestriandetectionandtrackingfromlowresolutionunmannedaerialvehiclethermalimagery
AT xuyongzheng pedestriandetectionandtrackingfromlowresolutionunmannedaerialvehiclethermalimagery
AT wangyunpeng pedestriandetectionandtrackingfromlowresolutionunmannedaerialvehiclethermalimagery