Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms
This 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...
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Format: | Book Chapter |
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KIT Scientific Publishing
2013
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Online Access: | Get Fullteks DOAB: description of the publication |
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LEADER | 01489naaaa2200265uu 4500 | ||
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001 | doab_20_500_12854_57007 | ||
005 | 20210211 | ||
020 | |a KSP/1000036064 | ||
020 | |a 9783731500810 | ||
024 | 7 | |a 10.5445/KSP/1000036064 |c doi | |
041 | 0 | |a English | |
042 | |a dc | ||
100 | 1 | |a Geiger, Andreas |4 auth | |
245 | 1 | 0 | |a Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms |
260 | |b KIT Scientific Publishing |c 2013 | ||
300 | |a 1 electronic resource (V, 162 p. p.) | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a This 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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by-sa/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-sa/4.0/ | ||
546 | |a English | ||
653 | |a computer vision | ||
653 | |a machine learning | ||
653 | |a scene understanding | ||
856 | 4 | 0 | |a www.oapen.org |u https://www.ksp.kit.edu/9783731500810 |7 0 |z Get Fullteks |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/57007 |7 0 |z DOAB: description of the publication |