Atmospheric visibility estimation and image contrast calibration

Hermansson, Patrik; Edstam, Klas

doi:10.1117/12.2241045

Cited by 1 publication

(3 citation statements)

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“…In addition, numerous studies contributed to the image contrast models which are based on contrast threshold and gradient [ 13 , 14 , 15 , 16 , 17 ]. Boussard, et al [ 13 ] focused on the study of low visibility condition.…”

Section: Related Workmentioning

confidence: 99%

“…The depth map of vehicle environments was obtained with onboard cameras, and the contrast threshold value (5%) was used for visibility estimations. Hermansson and Edstam [ 14 ] raised a contrast calibration method that changed the weather background of outdoor images and was capable of inspiring the visibility estimation. The weather parameters incorporated atmospheric conditions, illumination, visibility, etc.…”

Section: Related Workmentioning

confidence: 99%

“…Blackwell [ 5 ] explored observed threshold values of human eyes on account of a subjective observation method later. Based on [ 4 , 5 ], different kind of vision-based methods of visibility estimation are presented, such as the exploratory studies in the early stages [ 6 , 7 , 8 ], the luminance curves models [ 9 , 10 , 11 , 12 ], image contrast models [ 13 , 14 , 15 , 16 , 17 ], road sign models [ 18 , 19 , 20 , 21 , 22 , 23 ], regression models [ 24 , 25 ], etc. However, regarding practical applications, the current vision based methods are facing some challenges: (1) How should different visibility situations be handled?…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Total Bounded Variation Approach to Low Visibility Estimation on Expressways

Cheng

Yang

Liu

et al. 2018

Sensors

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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 (pseudo-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%.

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