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A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane...

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Autores principales: Xu, Yongzheng, Yu, Guizhen, Wang, Yunpeng, Wu, Xinkai, Ma, Yalong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017490/
https://www.ncbi.nlm.nih.gov/pubmed/27548179
http://dx.doi.org/10.3390/s16081325
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author Xu, Yongzheng
Yu, Guizhen
Wang, Yunpeng
Wu, Xinkai
Ma, Yalong
author_facet Xu, Yongzheng
Yu, Guizhen
Wang, Yunpeng
Wu, Xinkai
Ma, Yalong
author_sort Xu, Yongzheng
collection PubMed
description A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.
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spelling pubmed-50174902016-09-22 A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images Xu, Yongzheng Yu, Guizhen Wang, Yunpeng Wu, Xinkai Ma, Yalong Sensors (Basel) Article A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. MDPI 2016-08-19 /pmc/articles/PMC5017490/ /pubmed/27548179 http://dx.doi.org/10.3390/s16081325 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 Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Yongzheng
Yu, Guizhen
Wang, Yunpeng
Wu, Xinkai
Ma, Yalong
A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images
title A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images
title_full A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images
title_fullStr A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images
title_full_unstemmed A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images
title_short A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images
title_sort hybrid vehicle detection method based on viola-jones and hog + svm from uav images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017490/
https://www.ncbi.nlm.nih.gov/pubmed/27548179
http://dx.doi.org/10.3390/s16081325
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