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Understanding High-Level Semantics by Modeling Traffic Patterns

2013

Conference Paper

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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. All data and code will be made available upon publication.

Author(s): Hongyi Zhang and Andreas Geiger and Raquel Urtasun
Book Title: International Conference on Computer Vision
Pages: 3056-3063
Year: 2013
Month: December

Department(s): Autonomous Vision, Perceiving Systems
Research Project(s): Scene Understanding
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Address: Sydney, Australia

Links: pdf

BibTex

@inproceedings{Zhang2013ICCV,
  title = {Understanding High-Level Semantics by Modeling Traffic Patterns},
  author = {Zhang, Hongyi and Geiger, Andreas and Urtasun, Raquel},
  booktitle = {International Conference on Computer Vision},
  pages = {3056-3063},
  address = {Sydney, Australia},
  month = dec,
  year = {2013},
  month_numeric = {12}
}