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A variational approach to atmospheric visibility estimation in the weather of fog and haze

Cheng, Xiaogang (author)
Umeå universitet,KTH,Skolan för datavetenskap och kommunikation (CSC),Nanjing University of Posts and Telecommunications, Nanjing, China,Institutionen för tillämpad fysik och elektronik,Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing, Jiangsu, Peoples R China; Royal Inst Technol, Sch Comp Sci & Commun, Stockholm, Sweden
Yang, Bin (author)
Umeå universitet,Institutionen för tillämpad fysik och elektronik
Liu, G. (author)
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Olofsson, Thomas, 1968- (author)
Umeå universitet,Institutionen för tillämpad fysik och elektronik
Li, Haibo (author)
KTH,Skolan för datavetenskap och kommunikation (CSC)
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 (creator_code:org_t)
Elsevier, 2018
2018
English.
In: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 39, s. 215-224
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Real-time atmospheric visibility estimation in foggy and hazy weather plays a crucial role in ensuring traffic safety. Overcoming the inherent drawbacks with traditional optical estimation methods, like limited sampling volume and high cost, vision-based approaches have received much more attention in recent research on atmospheric visibility estimation. Based on the classical Koschmieder's formula, atmospheric visibility estimation is carried out by extracting an inherent extinction coefficient. In this paper we present a variational framework to handle the nature of time-varying extinction coefficient and develop a novel algorithm of extracting the extinction coefficient through a piecewise functional fitting of observed luminance curves. The developed algorithm is validated and evaluated with a big database of road traffic video from Tongqi expressway (in China). The test results are very encouraging and show that the proposed algorithm could achieve an estimation error rate of 10%. More significantly, it is the first time that the effectiveness of Koschmieder's formula in atmospheric visibility estimation was validated with a big dataset, which contains more than two million luminance curves extracted from real-world traffic video surveillance data.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Infrastrukturteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Infrastructure Engineering (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Naturresursteknik -- Annan naturresursteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Environmental Engineering -- Other Environmental Engineering (hsv//eng)

Keyword

Atmospheric visibility estimation
Computer vision
Fog and haze
Piecewise stationary time series
Variational approach

Publication and Content Type

ref (subject category)
art (subject category)

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