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A new spatio-temporal background–foreground bimodal for motion segmentation and detection in urban traffic scenes

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dc.FundingDetailsUniversiti Sains Islam Malaysia,�USIM PPPI/FKAB/0118/051000/14618 International Islamic University Malaysia,�IIUM
dc.FundingDetailsThis work was supported by University Science Islam Malaysia (USIM), Faculty of Engineering and Built Environment and Research and Innovation Management Center under the Research Grant Number: PPPI/FKAB/0118/051000/14618.
dc.contributor.affiliationsFaculty of Engineering and Built Environment
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorAl-Smadi M.en_US
dc.contributor.authorAbdulrahim K.en_US
dc.contributor.authorSeman K.en_US
dc.contributor.authorSalam R.A.en_US
dc.date.accessioned2024-05-29T02:06:00Z
dc.date.available2024-05-29T02:06:00Z
dc.date.issued2019
dc.description.abstractAutomatic vehicle detection in urban traffic surveillance is an important and urgent issue, since it provides necessaryinformation for further processing. Conventional techniques utilize either motion segmentation or appearance-baseddetection, which involves either poor adaptation or high computation. The complexity of urban traffic scenarios lies in slowmotion temporarily stopped or parked vehicles, dynamic background, and sudden illumination variations. In this paper, anew motion segmentation technique is proposed based on spatio-temporal background–foreground bimodal. The temporalbackground information is modeled using a weighted sigma–delta estimation, cumulative frame differencing is used tomodel the foreground pixels, and the spatial correlation between neighboring pixels is utilized to combine both backgroundand foreground models. The median of consecutive frame difference adapts sudden illumination variation, update back-ground model, and reinitialize foreground model. Comparative experimental results for typical urban traffic sequencesshow that the proposed technique achieves robust and accurate segmentation, which improves adaptation, reduce falsedetection, and satisfy real-time requirements. KeywordsMotion segmentation-Background subtraction-Cumulative frame differencing-Sigma–delta filter-Vehicle detectionen_US
dc.description.natureArticle in Pressen_US
dc.identifier.doi10.1007/s00521-019-04458-5
dc.identifier.isiWOS:000544784200039
dc.identifier.issn9410643
dc.identifier.scopus2-s2.0-85072172886
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072172886&doi=10.1007%2fs00521-019-04458-5&partnerID=40&md5=90c3f8fff558dd782a8a11df1adf3acd
dc.identifier.urihttps://link.springer.com/article/10.1007/s00521-019-04458-5
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10349
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherSpringer Londonen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.sourceScopus
dc.subjectBackground subtractionen_US
dc.subjectCumulative frame differencingen_US
dc.subjectMotion segmentationen_US
dc.subjectSigma-delta filteren_US
dc.subjectVehicle detectionen_US
dc.titleA new spatio-temporal background–foreground bimodal for motion segmentation and detection in urban traffic scenesen_US
dc.typeArticleen_US
dspace.entity.typePublication

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