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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

Tracking-Based Visibility Estimation

verfasst von : Stephan Lenor, Johannes Martini, Bernd Jähne, Ulrich Stopper, Stefan Weber, Florian Ohr

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Detection and Segmentation of Clustered Objects by Using Iterative Classification, Segmentation, and Gaussian Mixture Models and Application to Wood Log Detection
Nächstes Kapitel Predicting the Influence of Additional Training Data on Classification Performance for Imbalanced Data
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Metadaten
Titel
Tracking-Based Visibility Estimation
verfasst von
Stephan Lenor
Johannes Martini
Bernd Jähne
Ulrich Stopper
Stefan Weber
Florian Ohr
Copyright-Jahr
2014
Buch
Pattern Recognition

Print ISBN: 978-3-319-11751-5

Electronic ISBN: 978-3-319-11752-2

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-11752-2_29

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