Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model

In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tr...

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Autores principales:	Qianxia Cao, Zhengwu Wang, Kejun Long
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Transportation engineering TA1001-1280 Transportation and communications HE1-9990
Acceso en línea:	https://doaj.org/article/8c989fa1f6ff4597adb5774e13801f84
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id	oai:doaj.org-article:8c989fa1f6ff4597adb5774e13801f84
record_format	dspace
spelling	oai:doaj.org-article:8c989fa1f6ff4597adb5774e13801f842021-11-22T01:09:40ZTraffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model2042-319510.1155/2021/3515512https://doaj.org/article/8c989fa1f6ff4597adb5774e13801f842021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3515512https://doaj.org/toc/2042-3195In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tracking due to the dense and severe occlusion of vehicles. In this study, a novel background subtraction algorithm based on sparse representation is proposed to detect the traffic foreground at complex intersections to obtain traffic parameters. By establishing a novel background dictionary update model, the proposed method solves the problem that the background is easily contaminated by slow-moving or temporarily stopped vehicles and therefore cannot obtain the complete traffic foreground. Using the real-world urban traffic videos and the PV video sequences of i-LIDS, we first compare the proposed method with other detection methods based on sparse representation. Then, the proposed method is compared with other commonly used traffic foreground detection models in different urban intersection traffic scenarios. The experimental results show that the proposed method performs well in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles and has a good performance in both qualitative and quantitative evaluations.Qianxia CaoZhengwu WangKejun LongHindawi-WileyarticleTransportation engineeringTA1001-1280Transportation and communicationsHE1-9990ENJournal of Advanced Transportation, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Transportation engineering TA1001-1280 Transportation and communications HE1-9990
spellingShingle	Transportation engineering TA1001-1280 Transportation and communications HE1-9990 Qianxia Cao Zhengwu Wang Kejun Long Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model
description	In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tracking due to the dense and severe occlusion of vehicles. In this study, a novel background subtraction algorithm based on sparse representation is proposed to detect the traffic foreground at complex intersections to obtain traffic parameters. By establishing a novel background dictionary update model, the proposed method solves the problem that the background is easily contaminated by slow-moving or temporarily stopped vehicles and therefore cannot obtain the complete traffic foreground. Using the real-world urban traffic videos and the PV video sequences of i-LIDS, we first compare the proposed method with other detection methods based on sparse representation. Then, the proposed method is compared with other commonly used traffic foreground detection models in different urban intersection traffic scenarios. The experimental results show that the proposed method performs well in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles and has a good performance in both qualitative and quantitative evaluations.
format	article
author	Qianxia Cao Zhengwu Wang Kejun Long
author_facet	Qianxia Cao Zhengwu Wang Kejun Long
author_sort	Qianxia Cao
title	Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model
title_short	Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model
title_full	Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model
title_fullStr	Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model
title_full_unstemmed	Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model
title_sort	traffic foreground detection at complex urban intersections using a novel background dictionary learning model
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/8c989fa1f6ff4597adb5774e13801f84
work_keys_str_mv	AT qianxiacao trafficforegrounddetectionatcomplexurbanintersectionsusinganovelbackgrounddictionarylearningmodel AT zhengwuwang trafficforegrounddetectionatcomplexurbanintersectionsusinganovelbackgrounddictionarylearningmodel AT kejunlong trafficforegrounddetectionatcomplexurbanintersectionsusinganovelbackgrounddictionarylearningmodel
_version_	1718418412147834880

Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model

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