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dc.contributor.authorNamazi, Elnaz
dc.contributor.authorMester, Rudolf
dc.contributor.authorLu, Chaoru
dc.contributor.authorLi, Jingyue
dc.date.accessioned2022-10-12T10:59:19Z
dc.date.available2022-10-12T10:59:19Z
dc.date.created2022-06-06T10:36:09Z
dc.date.issued2022
dc.identifier.citationNeurocomputing. 2022, 499 35-46.en_US
dc.identifier.issn0925-2312
dc.identifier.urihttps://hdl.handle.net/11250/3025573
dc.description.abstractEstimating vehicles’ locations is one of the key components in intelligent traffic management systems (ITMSs) for increasing traffic scene awareness. Traditionally, stationary sensors have been employed in this regard. The development of advanced sensing and communication technologies on modern vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to estimate the traffic data of observed vehicles. This study aims to explore the capabilities of a monocular camera mounted on an MV in order to estimate the geolocation of the observed vehicle in a global positioning system (GPS) coordinate system. We proposed a new methodology by integrating deep learning, image processing, and geometric computation to address the observed-vehicle localization problem. To evaluate our proposed methodology, we developed new algorithms and tested them using real-world traffic data. The results indicated that our proposed methodology and algorithms could effectively estimate the observed vehicle’s latitude and longitude dynamically.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleGeolocation estimation of target vehicles using image processing and geometric computationen_US
dc.title.alternativeGeolocation estimation of target vehicles using image processing and geometric computationen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber35-46en_US
dc.source.volume499en_US
dc.source.journalNeurocomputingen_US
dc.identifier.doihttps://doi.org/10.1016/j.neucom.2021.10.127
dc.identifier.cristin2029626
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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