Document Type

Conference Proceeding

Source of Publication

Procedia Computer Science

Publication Date

1-1-2018

Abstract

© 2018 The Authors. Published by Elsevier Ltd. Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination of an ANN model and a set of global feature descriptors. Our experimental results show that the proposed solution presents very promising results in support for next-generation situational awareness and cooperative collision avoidance systems. Hence it can potentially contribute towards safer driving conditions in the presence of fog.

ISSN

1877-0509

Publisher

Elsevier B.V.

Volume

141

First Page

478

Last Page

483

Disciplines

Computer Sciences

Keywords

Computer vision, Convolution neural networks, Deep learning, Intelligent transportation systems, Machine learning, Meteorologcal visibility, Neural networks, Visibility distance

Scopus ID

85058290925

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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