CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS

CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS

The paper presents a method of classification of road traffic conditions based on the analysis of the content of images of the traffic flow. The view of the traffic lanes with vehicles is treated as a texture, while the change in the description of its characteristics is ascribed to the change in t...

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Main Author: Teresa PAMUŁA
Format: Article
Language:English
Published: Silesian University of Technology 2016-09-01
Series:Scientific Journal of Silesian University of Technology. Series Transport
Subjects:
traffic conditions
textures features
neural networks
Online Access:http://sjsutst.polsl.pl/archives/2016/vol92/101_SJSUTST92_2016_Pamula.pdf
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spelling doaj-acdbece8aa0b48cb900d082991e529882021-08-02T01:56:41ZengSilesian University of TechnologyScientific Journal of Silesian University of Technology. Series Transport0209-33242450-15492016-09-019210110910.20858/sjsutst.2016.92.10CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKSTeresa PAMUŁAThe paper presents a method of classification of road traffic conditions based on the analysis of the content of images of the traffic flow. The view of the traffic lanes with vehicles is treated as a texture, while the change in the description of its characteristics is ascribed to the change in the density of traffic. Four classes of conditions are determined on the basis of the values of Haralick texture features. An MLP network is used for classification. Video data, which were registered by an UAV hanging over a traffic junction, are used for validation of the method.http://sjsutst.polsl.pl/archives/2016/vol92/101_SJSUTST92_2016_Pamula.pdftraffic conditionstextures featuresneural networks
collection DOAJ
language English
format Article
sources DOAJ
author Teresa PAMUŁA
spellingShingle Teresa PAMUŁA
CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS
Scientific Journal of Silesian University of Technology. Series Transport
traffic conditions
textures features
neural networks
author_facet Teresa PAMUŁA
author_sort Teresa PAMUŁA
title CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS
title_short CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS
title_full CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS
title_fullStr CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS
title_full_unstemmed CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS
title_sort classification of road traffic conditions based on texture features of traffic images using neural networks
publisher Silesian University of Technology
series Scientific Journal of Silesian University of Technology. Series Transport
issn 0209-3324
2450-1549
publishDate 2016-09-01
description The paper presents a method of classification of road traffic conditions based on the analysis of the content of images of the traffic flow. The view of the traffic lanes with vehicles is treated as a texture, while the change in the description of its characteristics is ascribed to the change in the density of traffic. Four classes of conditions are determined on the basis of the values of Haralick texture features. An MLP network is used for classification. Video data, which were registered by an UAV hanging over a traffic junction, are used for validation of the method.
topic traffic conditions
textures features
neural networks
url http://sjsutst.polsl.pl/archives/2016/vol92/101_SJSUTST92_2016_Pamula.pdf
work_keys_str_mv AT teresapamuła classificationofroadtrafficconditionsbasedontexturefeaturesoftrafficimagesusingneuralnetworks
_version_ 1721244299283136512

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