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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.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*
virtual.oapen_relation_isPublishedBy.publisher_nameKIT Scientific Publishing
virtual.oapen_relation_isPublishedBy.publisher_websitehttp://www.ksp.kit.edu/
oapen.relation.isbn9783731500810*
oapen.pagesV, 162 p.*
oapen.volume025*


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