Estimating Atmospheric Visibility Using General-Purpose Cameras

Xie, Ling; Chiu, Alex; Newsam, Shawn

doi:10.1007/978-3-540-89646-3_35

Cited by 28 publications

(16 citation statements)

References 5 publications

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“…Nevertheless, we have shown that taking Lambertian areas into account is more robust than relying on the whole image gradients. In this way, we improve the previous methods proposed by Xie et al (2008) and Liaw et al (2009). Compared to previous methods, we are able to estimate visibility ranges down to very short ranges like (Hautière et al, 2008), as well as up Figure 8: Geometry related to the luminous intensity distribution of a road stud at grazing angle ε.…”

Section: Qualitative Comparison With Other Methodsmentioning

confidence: 95%

“…Other approaches which exploit machine learning seek to find the frequency response characteristics linking the image with visibility data. Indeed, Xie et al (2008) and Liaw et al (2010) seek the linear correlation between some indicator of contrast and meteorological visibility data. Xie et al (2008) applies a low pass filter to the Fourier transform of the image.…”

Section: Related Researchmentioning

confidence: 99%

“…Indeed, Xie et al (2008) and Liaw et al (2010) seek the linear correlation between some indicator of contrast and meteorological visibility data. Xie et al (2008) applies a low pass filter to the Fourier transform of the image. Hagiwara et al (2006) also propose the WIPS frequency operator (Weighted Intensity Power Spectrum) which was proven to be well correlated with human perception.…”

Section: Related Researchmentioning

confidence: 99%

“…This allows using Equation 12 and also corresponds to precedent in the literature (Xie et al, 2008;Liaw et al, 2009). Now, let us consider the estimator of visibility E L based on the sum of the module of Sobel gradient, weighted by the confidence P L of the pixel to belong to a Lambertian area, defined by Equation 13.…”

Section: Visibility Estimation Based On Robust Gradientmentioning

confidence: 99%

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Visibility monitoring using conventional roadside cameras – Emerging applications

Babari

Hautiére

Dumont

et al. 2012

Transportation Research Part C: Emerging Technologies

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The measurement of atmospheric visibility is an important element for road and air transportation safety. We propose in this paper a novel estimator of the atmospheric visibility by already existing conventional highway cameras, with a technique based on the gradient magnitude selected by applying Lambert's law with respect to changes in lighting conditions. The response of this estimator is calibrated by non-linear regression with data from a visibility meter installed in a test site which has been instrumented with a camera. Through our technique, atmospheric visibility estimates are obtained with an average error of 30% for images taken in the day, with sky luminance between 10 to 8,000 cd.m −2 and visibility distances up to 15 km. Our results allow us to envision practical implementation on roadsides in the near future to determine local visibility for the benefit of road safety, meteorological observation and air quality monitoring.

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Section: Qualitative Comparison With Other Methodsmentioning

confidence: 95%

Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Section: Visibility Estimation Based On Robust Gradientmentioning

confidence: 99%

See 2 more Smart Citations

Visibility monitoring using conventional roadside cameras – Emerging applications

Babari

Hautiére

Dumont

et al. 2012

Transportation Research Part C: Emerging Technologies

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“…The method proposed in this paper belongs to this second family. Usually, a simple gradient based on the Sobel filter or a high-pass filter in the frequency domain are used to compute the contrast [6][7][8]. Luo et al [9] have shown that the visual range obtained with both approaches are highly correlated.…”

Section: Introductionmentioning

confidence: 99%

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

Hautiére

Babari

Dumont

et al. 2011

Lecture Notes in Computer Science

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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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