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210512 ||| eng |
| 020 |
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|a 1000036064
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| 020 |
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|a 9783731500810
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| 100 |
1 |
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|a Geiger, Andreas
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| 245 |
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|a Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms
|h Elektronische Ressource
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| 260 |
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|b KIT Scientific Publishing
|c 2013
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| 300 |
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|a 1 electronic resource (V, 162 p. p.)
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| 653 |
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|a machine learning
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| 653 |
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|a computer vision
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| 653 |
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|a scene understanding
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 490 |
0 |
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|a Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-sa/4.0/
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| 028 |
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|a 10.5445/KSP/1000036064
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| 856 |
4 |
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|u https://www.ksp.kit.edu/9783731500810
|7 0
|x Verlag
|3 Volltext
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| 856 |
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2 |
|u https://directory.doabooks.org/handle/20.500.12854/57007
|z DOAB: description of the publication
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| 520 |
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|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.
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