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dc.contributor.authorGeiger, Andreas*
dc.date.accessioned2021-02-11T23:55:10Z
dc.date.available2021-02-11T23:55:10Z
dc.date.issued2013*
dc.date.submitted2019-07-30 20:01:58*
dc.identifier34637*
dc.identifier.issn16134214*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/57007
dc.description.abstractThis work is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences.*
dc.languageEnglish*
dc.relation.ispartofseriesSchriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie*
dc.subjectQA75.5-76.95*
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer scienceen_US
dc.subject.othercomputer vision*
dc.subject.othermachine learning*
dc.subject.otherscene understanding*
dc.titleProbabilistic Models for 3D Urban Scene Understanding from Movable Platforms*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000036064*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783731500810*
oapen.pagesV, 162 p.*
oapen.volume025*
peerreview.review.typeFull text
peerreview.anonymityAll identities known
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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