Automatic Map Update Using Dashcam Videos

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorZhanabatyrova, Azizaen_US
dc.contributor.authorSouza Leite, Claytonen_US
dc.contributor.authorXiao, Yuen_US
dc.contributor.departmentDepartment of Information and Communications Engineeringen
dc.contributor.groupauthorMobile Cloud Computingen
dc.contributor.organizationMobile Cloud Computingen_US
dc.date.accessioned2023-08-11T07:22:38Z
dc.date.available2023-08-11T07:22:38Z
dc.date.issued2023-07-01en_US
dc.descriptionPublisher Copyright: Author | openaire: EC/H2020/825496/EU//5G-MOBIX
dc.description.abstractAutonomous driving requires 3D maps that provide accurate and up-to-date information about semantic landmarks. Since cameras present wider availability and lower cost compared with laser scanners, vision-based mapping solutions, especially the ones using crowdsourced visual data, have attracted much attention from academia and industry. However, previous works have mainly focused on creating 3D point clouds, leaving automatic change detection as open issue. We propose a pipeline for initiating and updating 3D maps with dashcam videos, with a focus on automatic change detection based on comparison of metadata (e.g., the types and locations of traffic signs). To improve the performance of metadata generation, which depends on the accuracy of 3D object detection and localization, we introduce a novel deep learning-based pixel-wise 3D localization algorithm. The algorithm, trained directly with SfM (Structure from Motion) point cloud data, accurately locates objects in 3D space by estimating not only depth from monocular images but also lateral and height distances. In addition, we also propose a point clustering and thresholding algorithm to improve the robustness of the system to errors. We have performed experiments with different types of cameras, lighting, and weather conditions. The changes were detected with an average accuracy above 90%. The errors in the campus area were mainly due to traffic signs seen from a far distance to the vehicle and intended for pedestrians and cyclists only. We also conducted cause analysis of the detection and localization errors to measure the impact from the performance of the background technology in use.en
dc.description.versionPeer revieweden
dc.format.extent19
dc.format.extent11825-11843
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationZhanabatyrova, A, Souza Leite, C & Xiao, Y 2023, ' Automatic Map Update Using Dashcam Videos ', IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11825-11843 . https://doi.org/10.1109/JIOT.2023.3244693en
dc.identifier.doi10.1109/JIOT.2023.3244693en_US
dc.identifier.issn2327-4662
dc.identifier.otherPURE UUID: 7469d598-7eb0-40fa-9c16-ea7086bddd78en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/7469d598-7eb0-40fa-9c16-ea7086bddd78en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85149415790&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/118051777/Automatic_Map_Update_Using_Dashcam_Videos.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/122366
dc.identifier.urnURN:NBN:fi:aalto-202308114715
dc.language.isoenen
dc.publisherIEEE
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/825496/EU//5G-MOBIXen_US
dc.relation.ispartofseriesIEEE Internet of Things Journalen
dc.relation.ispartofseriesVolume 10, issue 13en
dc.rightsopenAccessen
dc.subject.keywordautonomous drivingen_US
dc.subject.keywordCamerasen_US
dc.subject.keywordchange detectionen_US
dc.subject.keywordlocalizationen_US
dc.subject.keywordLocation awarenessen_US
dc.subject.keywordmappingen_US
dc.subject.keywordPipelinesen_US
dc.subject.keywordPoint cloud compressionen_US
dc.subject.keywordSemantic segmentationen_US
dc.subject.keywordSemanticsen_US
dc.subject.keywordstructure from motionen_US
dc.subject.keywordThree-dimensional displaysen_US
dc.titleAutomatic Map Update Using Dashcam Videosen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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