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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/26323

Title: A Neural network approach to visibility range estimation under foggy weather conditions
Authors: Chaabani, Hazar
Kamoun, Faouzi
Bargaoui, Hichem
Outay, Fatma
Yasar, Ansar-Ul-Haque
Issue Date: 2017
Publisher: Elsevier BV
Citation: Shakshuki, Elhadi (Ed.). The 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2017) / The 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2017) / Affiliated Workshops, Elsevier BV,p. 466-471
Series/Report: Procedia Computer Science
Series/Report no.: 113
Abstract: The degradation of visibility due to foggy weather conditions is a common trigger for road accidents and, as a result, there has been a growing interest to develop intelligent fog detection and visibility range estimation systems. In this contribution, we provide a brief overview of the state-of-the-art contributions in relation to estimating visibility distance under foggy weather conditions. We then present a neural network approach for estimating visibility distances using a camera that can be fixed to a roadside unit (RSU) or mounted onboard a moving vehicle. We evaluate the proposed solution using a diverse set of images under various fog density scenarios. Our approach shows very promising results that outperform the classical method of estimating the maximum distance at which a selected target can be seen. The originality of the approach stems from the usage of The degradation of visibility due to foggy weather conditions is a common trigger for road accidents and, as a result, there has been a growing interest to develop intelligent fog detection and visibility range estimation systems. In this contribution, we provide a brief overview of the state-of-the-art contributions in relation to estimating visibility distance under foggy weather conditions. We then present a neural network approach for estimating visibility distances using a camera that can be fixed to a roadside unit (RSU) or mounted onboard a moving vehicle. We evaluate the proposed solution using a diverse set of images under various fog density scenarios. Our approach shows very promising results that outperform the classical method of estimating the maximum distance at which a selected target can be seen. The originality of the approach stems from the usage of a single camera and a neural network learning phase based on a hybrid global feature descriptor. The proposed method can be applied to support next-generation cooperative hazard & incident warning systems based on I2V, I2I and V2V communications. (c) 2017 The Authors. Published by Elsevier B.V.
Notes: Kamoun, F (reprint author), ESPRIT Sch Engn, ZI Chotrana 2,POB 160, Tunis, Tunisia. faouzi.kammoun@esprit.tn
URI: http://hdl.handle.net/1942/26323
DOI: 10.1016/j.procs.2017.08.304
ISI #: 000419236500061
ISSN: 1877-0509
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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