LEADER 01634nam  2200349z- 450 
001 9910688420403321
005 20210211
010   $a1000036064
035   $a(CKB)4920000000101903
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/57007
035   $a(oapen)doab57007
035   $a(EXLCZ)994920000000101903
100   $a20202102d2013      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aProbabilistic Models for 3D Urban Scene Understanding from Movable Platforms
210   $cKIT Scientific Publishing$d2013
215   $a1 online resource (V, 162 p. p.)
225 1 $aSchriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie
311 08$a3-7315-0081-7 
330   $aThis 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.
610   $acomputer vision
610   $amachine learning
610   $ascene understanding
700   $aGeiger$b Andreas$4auth$01352378
906   $aBOOK
912   $a9910688420403321
996   $aProbabilistic Models for 3D Urban Scene Understanding from Movable Platforms$93173961
997   $aUNINA