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Title: | A Neural network approach to visibility range estimation under foggy weather conditions | Authors: | Chaabani, Hazar Kamoun, Faouzi Bargaoui, Hichem Outay, Fatma YASAR, Ansar |
Issue Date: | 2017 | Publisher: | Elsevier BV | Source: | 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 | Keywords: | visibility distance; fog detection; intelligent transportation systems; meteorologcal visibility; driving assistance; neural networks; machine learning; Koschmieder Law; computer vision; Fourier Transform | Document URI: | http://hdl.handle.net/1942/26323 | DOI: | 10.1016/j.procs.2017.08.304 | ISI #: | 000419236500061 | Rights: | © 2017 The Authors. Published by Elsevier B.V | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2019 |
Appears in Collections: | Research publications |
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chaabani 1.pdf | Published version | 747.79 kB | Adobe PDF | View/Open |
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