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A Total Bounded Variation Approach to Low Visibility Estimation on Expressways
KTH, School of Electrical Engineering and Computer Science (EECS). Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China.;Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden.
Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden.;Xian Univ Architecture & Technol, Sch Environm & Municipal Engn, Xian 710055, Shaanxi, Peoples R China..
Nanjing Tech Univ, Sch Phys & Math Sci, Nanjing 211800, Jiangsu, Peoples R China..
Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden..
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2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 2, article id 392Article in journal (Refereed) Published
Abstract [en]

Low visibility on expressways caused by heavy fog and haze is a main reason for traffic accidents. Real-time estimation of atmospheric visibility is an effective way to reduce traffic accident rates. With the development of computer technology, estimating atmospheric visibility via computer vision becomes a research focus. However, the estimation accuracy should be enhanced since fog and haze are complex and time-varying. In this paper, a total bounded variation (TBV) approach to estimate low visibility (less than 300 m) is introduced. Surveillance images of fog and haze are processed as blurred images (pse udo-blurred images), while the surveillance images at selected road points on sunny days are handled as clear images, when considering fog and haze as noise superimposed on the clear images. By combining image spectrum and TBV, the features of foggy and hazy images can be extracted. The extraction results are compared with features of images on sunny days. Firstly, the low visibility surveillance images can be filtered out according to spectrum features of foggy and hazy images. For foggy and hazy images with visibility less than 300 m, the high-frequency coefficient ratio of Fourier (discrete cosine) transform is less than 20%, while the low-frequency coefficient ratio is between 100% and 120%. Secondly, the relationship between TBV and real visibility is established based on machine learning and piecewise stationary time series analysis. The established piecewise function can be used for visibility estimation. Finally, the visibility estimation approach proposed is validated based on real surveillance video data. The validation results are compared with the results of image contrast model. Besides, the big video data are collected from the Tongqi expressway, Jiangsu, China. A total of 1,782,000 frames were used and the relative errors of the approach proposed are less than 10%.

Place, publisher, year, edition, pages
MDPI , 2018. Vol. 18, no 2, article id 392
Keywords [en]
total bounded variation, image spectrum, low visibility estimation, piece stationary, fog and haze
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:kth:diva-226812DOI: 10.3390/s18020392ISI: 000427544000075PubMedID: 29382181Scopus ID: 2-s2.0-85041434425OAI: oai:DiVA.org:kth-226812DiVA, id: diva2:1201752
Note

QC 20180426

Available from: 2018-04-26 Created: 2018-04-26 Last updated: 2018-04-26Bibliographically approved

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