Abstract
Assessing atmospheric visibility conditions is a challenging and increasingly important task not only in the context of video-based driver assistance systems. As a commonly used quantity, meteorological visibility describes the visual range for observations through scattering and absorbing aerosols such as fog or smog.
We present a novel algorithm for estimating meteorological visibility based on object tracks in camera images. To achieve this, we introduce a likelihood objective function based on Koschmieder’s model for horizontal vision to derive the atmospheric extinction coefficient from the objects’ luminances and distances provided by the tracking. To make this algorithm applicable for real-time purposes, we propose an easy-to-implement and extremely fast minimization method which clearly outperforms classical methods such as Levenberg-Marquardt. Our approach is tested with promising results on real-world sequences recorded with a commercial driver assistance camera as well as on artificial images generated by Monte-Carlo simulations.
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References
ARM: Specification of Cortex-A9 Floating-Point Unit (1.5 GHz), Revision: r2p2 (2010)
Babari, R., Hautière, N., Dumont, É., Brémond, R., Paparoditis, N.: A model-driven approach to estimate atmospheric visibility with ordinary cameras. Atmos. Environ. 45(30), 5316–5324 (2011)
Boussard, C., Hautière, N., d’Andréa Novel, B.: Visibility distance estimation based on structure from motion. In: Proceedings of the 11th International Conference on Control Automation, Robotics and Vision, pp. 1416–1421. IEEE, Dec 2010
Bronte, S., Bergasa, L.M., Alcantarilla, P.F.: Fog detection system based on computer vision techniques. In: Proceedings of the 12th International Conference on Intelligent Transportation Systems, pp. 1–6. IEEE, Oct 2009
Busch, C., Debes, E.: Wavelet transform for analyzing fog visibility. IEEE Intell. Syst. Appl. 13(6), 66–71 (1998)
Chandrasekhar, S.: Radiative Transfer. Dover Publications, New York (1960)
CIE: Termlist (2012). http://eilv.cie.co.at/
Croft, P.J.: Fog. In: Holton, J.R. (ed.) Encyclopedia of Atmospheric Sciences. Academic Press, New York (2003)
European Machine Vision Association, et al.: EMVA Standard 1288, Standard for Characterization of Image Sensors and Cameras (2010)
Geng, W., Lu, X., Yang, L., Chen, W., Liu, Y.: Detection algorithm of video image distance based on rectangular pattern. In: Proceedings of the 5th International Congress on Image and Signal Processing, pp. 856–860. IEEE, Oct 2012
Hautière, N., Labayrade, R., Aubert, D.: Real-time disparity contrast combination for onboard estimation of the visibility distance. IEEE Trans. Intell. Transp. Syst. 7(2), 201–212 (2006)
Hautière, N., Tarel, J.P., Lavenant, J., Aubert, D.: Automatic fog detection and estimation of visibility distance through use of an onboard camera. Mach. Vis. Appl. 17(1), 8–20 (2006)
Koschmieder, H.: Theorie der Horizontalen Sichtweite. Physik der Freien Atmosphäre 12, 33–55 (1924)
Lenor, S., Jähne, B., Weber, S., Stopper, U.: An improved model for estimating the meteorological visibility from a road surface luminance curve. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 184–193. Springer, Heidelberg (2013)
Middleton, W.E.K.: Vision Through the Atmosphere. University of Toronto Press, Toronto (1952)
Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vision 48(3), 233–254 (2002)
Narasimhan, S.G., Nayar, S.K.: Shedding light on the weather. In: Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 665–672. IEEE, June 2003
Negru, M., Nedevschi, S.: Image based fog detection and visibility estimation for driving assistance systems. In: Proceedings of the International Conference on Intelligent Computer Communication and Processing, pp. 163–168. IEEE, Sept 2013
Pavlić, M., Belzner, H., Rigoll, G., Ilić, S.: Image based fog detection in vehicles. In: Proceedings of the Intelligent Vehicles Symposium, pp. 1132–1137. IEEE, June 2012
Pomerleau, D.: Visibility estimation from a moving vehicle using the RALPH vision system. In: Proceedings of the Conference on Intelligent Transportation System, pp. 906–911. IEEE, Nov 1997
Slama, C.C., Theurer, C., Henriksen, S.W., et al.: Manual of Photogrammetry, 5th edn. American Society of Photogrammetry, Falls Church (1980)
Song, H., Chen, Y., Gao, Y.: Homogenous fog condition recognition based on traffic scene. In: Proceedings of the International Conference on Modelling, Identification and Control, pp. 612–617. IEEE, June 2012
VDI-Kommission Reinhaltung der Luft: VDI 3786 part 6: Meteorological Measurements of Air Pollution, Turbidity of Ground-Level Atmosphere, Standard Visibility (1983)
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Lenor, S., Martini, J., Jähne, B., Stopper, U., Weber, S., Ohr, F. (2014). Tracking-Based Visibility Estimation. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_29
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DOI: https://doi.org/10.1007/978-3-319-11752-2_29
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