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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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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