NDLI: Understanding High-Level Semantics by Modeling Traffic Patterns

Content Provider	IEEE Xplore Digital Library
Author	Hongyi Zhang Geiger, A. Urtasun, R.
Copyright Year	2013
Description	Author affiliation: TTI Chicago, Chicago, IL, USA (Urtasun, R.) \|\| MPI Tubingen, Tubingen, Germany (Geiger, A.) \|\| Peking Univ., Beijing, China (Hongyi Zhang)
Abstract	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.
Sponsorship	IEEE Comput. Soc.
Starting Page	3056
Ending Page	3063
File Size	2278293
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.379
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Vehicles Geometry Splines (mathematics) Roads Semantics Solid modeling
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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