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Understanding Road Scenes Using Visual Cues and GPS Information

  • Jose M. Alvarez
  • Felipe Lumbreras
  • Antonio M. Lopez
  • Theo Gevers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

Understanding road scenes is important in computer vision with different applications to improve road safety (e.g., advanced driver assistance systems) and to develop autonomous driving systems (e.g., Google driver-less vehicle). Current vision–based approaches rely on the robust combination of different technologies including color and texture recognition, object detection, scene context understanding. However, the performance of these approaches drops–off in complex acquisition conditions with reduced visibility (e.g., dusk, dawn, night) or adverse weather conditions (e.g., rainy, snowy, foggy). In these adverse situations any prior information about the scene is relevant to constraint the process. Therefore, in this demo we show a novel approach to obtain on–line prior information about the road ahead a moving vehicle to improve road scene understanding algorithms. This combination exploits the robustness of digital databases and the adaptation of algorithms based on visual information acquired in real time. Experimental results in challenging road scenarios show the applicability of the algorithm to improve vision–based road scene understanding algorithms. Furthermore, the algorithm can also be applied to correct imprecise road information in the database.

Keywords

Adverse Weather Condition Digital Database Advanced Driver Assistance System Road Scene Line Road 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Alvarez, J.M., Gevers, T., Lopez, A.M.: 3d scene priors for road detection. In: CVPR 2010, pp. 57–64 (2010)Google Scholar
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    Alvarez, J.M., Gevers, T., Lopez, A.M.: Combining context cues and priors for road detection. Under review in PAMI (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jose M. Alvarez
    • 1
  • Felipe Lumbreras
    • 1
  • Antonio M. Lopez
    • 1
  • Theo Gevers
    • 1
    • 2
  1. 1.Computer Vision CenterUniversitat Autònoma de BarcelonaBarcelonaSpain
  2. 2.Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands

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