Understanding High-Level Semantics by Modeling Traffic Patterns

In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-le...

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Bibliographic Details
Published in2013 IEEE International Conference on Computer Vision pp. 3056 - 3063
Main Authors Hongyi Zhang, Geiger, Andreas, Urtasun, Raquel
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Autonomous
Computer vision
Conferences
Geometry
Outdoor
Roads
Semantics
Solid modeling
Splines (mathematics)
State of the art
Three dimensional models
Three-dimensional displays
Traffic engineering
Traffic flow
Vehicles
Online AccessGet full text
ISSN1550-5499
DOI10.1109/ICCV.2013.379

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Summary:In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-level semantics in the form of traffic patterns. We found that a small number of patterns is sufficient to model the vast majority of traffic scenes and show how these patterns can be learned. As evidenced by our experiments, this high-level reasoning significantly improves the overall scene estimation as well as the vehicle-to-lane association when compared to state-of-the-art approaches.
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SourceType-Conference Papers & Proceedings-2
ISSN:1550-5499
DOI:10.1109/ICCV.2013.379