© The Institution of Engineering and Technology
A fog level estimation method using intensity curves arranged with geometrical information is proposed. The curves, extracted from pixels in the road region and reflecting the tendency of intensity convergence under foggy conditions, are provided to the stacked auto-encoders and the encoded features are classified into four fog levels by a neural network. Experimental results with clear and foggy road images of various places show that the intensity curve feature of the proposed method is effectively working not only in detecting the presence of fog, but also in estimating the fog level accurately.
References
-
-
1)
-
3. Negru, M., Nedevschi, S.: ‘Image based fog detection and visibility estimation for driving assistance systems’. 2013 IEEE 9th Int. Conf. Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 2013, pp. 163–168.
-
2)
-
7. Danielsson, P.-E., Seger, O.: ‘Generalized and separable Sobel operators’. inFreeman, H. (ED.): ‘Machine vision for three-dimensional scenes, (Academic press, Piscataway, NJ, USA, 1990), pp. 347–379.
-
3)
-
28. Holland, J.: ‘Genetic algorithms’, Sci. Am., 1992, 267, (1), pp. 66–72 (doi: 10.1038/scientificamerican0792-66).
-
4)
-
13. Cheng, Y.: ‘Mean shift, mode seeking and clustering’, IEEE Trans. Pattern Anal. Mach. Intell., 1995, 17, (8), pp. 790–799 (doi: 10.1109/34.400568).
-
5)
-
4. Duda, R.O., Hart, P.E.: ‘Use of the Hough transformation to detect lines and curves in pictures’, Commun. ACM, 1972, 15, (1), pp. 11–15 (doi: 10.1145/361237.361242).
-
6)
-
8. Guo, Z., Hall, R.W.: ‘Parallel thinning with two-subiteration algorithms’, Commun. ACM, 1989, 21, (3), pp. 359–373 (doi: 10.1145/62065.62074).
-
7)
-
1. Liu, C., Lu, X., Ji, S., et al: ‘A fog level detection method based on image hsv color histogram’. 2014 IEEE Int. Conf. Progress in Informatics and Computing, Shanghai, China, May, 2014, pp. 373–377.
-
8)
-
13. Middleton, W.E.K.: ‘Vision through the atmosphere’, in Bartels, J. (ED.): Geophysik II/Geophysics II (Springer Berlin Heidelberg, Berlin, Heidelberg, 1957), pp. 254–287.
-
9)
-
12. Song, K.T., Tai, J.C.: ‘Dynamic calibration of pan-tilt-zoom cameras for traffic monitoring’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2006, 36, (5), pp. 1091–1103 (doi: 10.1109/TSMCB.2006.872271).
-
10)
-
6. Wren, C., Azarbayejani, A., Darrell, T., et al: ‘Pfinder: real-time tracking of the human body’. Proc. of the Second Int. Conf. Automatic Face and Gesture Recognition, Killington, VT, USA, October 1996, pp. 51–56.
-
11)
-
5. Reby, D., Lek, S., Dimopoulos, I., et al: ‘Artificial neural networks as a classification method in the behavioural sciences’, Behav. Processes, 1997, 40, (1), pp. 35–43 (doi: 10.1016/S0376-6357(96)00766-8).
-
12)
-
2. Pavlic, M., Rigoll, G., Ilic, S.: ‘Classification of images in fog and fog-free scenes for use in vehicles’. 2013 IEEE Intelligent Vehicles Symp. (IV), Gold Coast, Australia, June 2013, pp. 481–486.
-
13)
-
4. Ng, A.Y.: ‘Sparse autoencoder’. , Stanford University, 2011.
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