Abstract
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.
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Hautiére, N., Babari, R., Dumont, É., Brémond, R., Paparoditis, N. (2011). Estimating Meteorological Visibility Using Cameras: A Probabilistic Model-Driven Approach. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_20
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DOI: https://doi.org/10.1007/978-3-642-19282-1_20
Publisher Name: Springer, Berlin, Heidelberg
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