Long-term image-based vehicle localization improved with learnt semantic descriptors

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dc.authorid0000-0001-8712-9461en_US
dc.contributor.authorÇınaroğlu, İbrahim
dc.contributor.authorBaştanlar, Yalın
dc.date.accessioned2022-03-08T06:19:16Z
dc.date.available2022-03-08T06:19:16Z
dc.date.issued2022en_US
dc.departmentKMÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionWOS:000807515200009en_US
dc.description.abstractVision based solutions for the localization of vehicles have become popular recently. In this study, we employ an image retrieval based visual localization approach, in which database images are kept with GPS coordinates and the location of the retrieved database image serves as the position estimate of the query image in a city scale driving scenario. Regarding this approach, most existing studies only use descriptors extracted from RGB images and do not exploit semantic content. We show that localization can be improved via descriptors extracted from semantically segmented images, especially when the environment is subjected to severe illumination, seasonal or other long-term changes. We worked on two separate visual localization datasets, one of which (Malaga Streetview Challenge) has been generated by us and made publicly available. Following the extraction of semantic labels in images, we trained a CNN model for localization in a weakly-supervised fashion with triplet ranking loss. The optimized semantic descriptor can be used on its own for localization or preferably it can be used together with a state-of-the-art RGB image based descriptor in hybrid fashion to improve accuracy. Our experiments reveal that the proposed hybrid method is able to increase the localization performance of the standard (RGB image based) approach up to 7.7% regarding Top-1 Recall values.en_US
dc.identifier.citationCinaroglu, I., & Bastanlar, Y. (2022). Long-term image-based vehicle localization improved with learnt semantic descriptors. Engineering Science and Technology, an International Journal, 35 doi:10.1016/j.jestch.2022.101098en_US
dc.identifier.doi10.1016/j.jestch.2022.101098
dc.identifier.issn2215-0986
dc.identifier.scopus2-s2.0-85125251322
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2022.101098
dc.identifier.urihttps://hdl.handle.net/11492/6171
dc.identifier.volume35en_US
dc.identifier.wosWOS:000807515200009
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.institutionauthorÇınaroğlu, İbrahim
dc.language.isoen
dc.publisherElsevier B.V.en_US
dc.relation.journalEngineering Science and Technology, an International Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAutonomous Drivingen_US
dc.subjectImage Matchingen_US
dc.subjectImage-Based Localizationen_US
dc.subjectSemantic Descriptoren_US
dc.subjectSemantic Segmentationen_US
dc.titleLong-term image-based vehicle localization improved with learnt semantic descriptorsen_US
dc.typeArticle

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