Estimating Meteorological Visibility Using Cameras: A Probabilistic Model-Driven Approach

  • Nicolas Hautiére
  • Raouf Babari
  • Éric Dumont
  • Roland Brémond
  • Nicolas Paparoditis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6495)


Estimating the atmospheric or meteorological visibility distance is very important for air and ground transport safety, as well as for air quality. However, there is no holistic approach to tackle the problem by camera. Most existing methods are data-driven approaches, which perform a linear regression between the contrast in the scene and the visual range estimated by means of reference additional sensors. In this paper, we propose a probabilistic model-based approach which takes into account the distribution of contrasts in the scene. It is robust to illumination variations in the scene by taking into account the Lambertian surfaces. To evaluate our model, meteorological ground truth data were collected, showing very promising results. This works opens new perspectives in the computer vision community dealing with environmental issues.


Illumination Variation Background Luminance Visibility Distance Computer Vision Community Lambertian Surface 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nicolas Hautiére
    • 1
  • Raouf Babari
    • 1
  • Éric Dumont
    • 1
  • Roland Brémond
    • 1
  • Nicolas Paparoditis
    • 2
  1. 1.LEPSIS, INRETS-LCPCUniversité Paris-EstParisFrance
  2. 2.MATIS, IGNUniversité Paris-EstSaint-MandéFrance

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