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http://hdl.handle.net/1942/30407
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DC Field | Value | Language |
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dc.contributor.author | Outay, Fatma | - |
dc.contributor.author | Taha, Bilal | - |
dc.contributor.author | Chaabani, Hazar | - |
dc.contributor.author | Kamoun, Faouzi | - |
dc.contributor.author | Werghi, Naoufel | - |
dc.contributor.author | YASAR, Ansar | - |
dc.date.accessioned | 2020-01-24T12:37:10Z | - |
dc.date.available | 2020-01-24T12:37:10Z | - |
dc.date.issued | 2021 | - |
dc.date.submitted | 2020-01-23T14:06:09Z | - |
dc.identifier.citation | PERSONAL AND UBIQUITOUS COMPUTING, 25, p. 51-62 | - |
dc.identifier.issn | 1617-4909 | - |
dc.identifier.uri | http://hdl.handle.net/1942/30407 | - |
dc.description.abstract | Next-generation intelligent transportation systems are based on the acquisition of ambient data that influence traffic flow and safety. Among these, is the ambient visibility range whose estimation, in the presence of fog, is extremely useful for next-generation intelligent transportation systems. However, existing camera-based approaches are based on "engineered features" extraction methods that use computer algorithms and procedures from the image processing field. In this contribution, a novel approach to estimate visibility range under foggy weather conditions is proposed which is based on "learned features" instead. More precisely, we use AlexNet deep convolutional neural network (DCNN), trained with raw image data, for feature extraction and a support vector machine (SVM) for visibility range estimation. Our quantitative analysis showed that the proposed approach is very promising in estimating the visibility range with very good accuracy. The proposed solution can pave the way towards intelligent driveway assistance systems to enhance awareness of driving weather conditions and hence mitigate the safety risks emanating from fog-induced low visibility conditions. | - |
dc.description.sponsorship | This research was financially supported by Zayed University Cluster Research Grant No. R17075. | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER LONDON LTD | - |
dc.rights | Springer-Verlag London Ltd., part of Springer Nature 2019 | - |
dc.subject.other | Intelligenttransportation systems | - |
dc.subject.other | Ubiquitoustechnologies | - |
dc.subject.other | Atmospheric visibility | - |
dc.subject.other | Road safety | - |
dc.subject.other | Deepconvolutional neural networks | - |
dc.subject.other | Ambient intelligence | - |
dc.title | Estimating ambient visibility in the presence of fog: a deep convolutional neural network approach | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 62 | - |
dc.identifier.spage | 51 | - |
dc.identifier.volume | 25 | - |
local.format.pages | 12 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Kamoun, F (reprint author), ESPRIT Sch Engn, Tunis, Tunisia. | - |
dc.description.notes | Fatma.outay@zu.ac.ae; bilal.taha@mail.utoronto.ca; | - |
dc.description.notes | hazar.chaabani@esprit.tn; faouzi.kammoun@esprit.tn; | - |
dc.description.notes | Naoufel.Werghi@ku.ac.ae; ansar.yasar@uhasselt.be | - |
dc.description.other | Kamoun, F (reprint author), ESPRIT Sch Engn, Tunis, Tunisia. Fatma.outay@zu.ac.ae; bilal.taha@mail.utoronto.ca; hazar.chaabani@esprit.tn; faouzi.kammoun@esprit.tn; Naoufel.Werghi@ku.ac.ae; ansar.yasar@uhasselt.be | - |
local.publisher.place | 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.source.type | Article | - |
dc.identifier.doi | 10.1007/s00779-019-01334-w | - |
dc.identifier.isi | WOS:000494801100002 | - |
dc.contributor.orcid | KAMOUN, FAOUZI/0000-0002-3740-1452 | - |
dc.identifier.eissn | 1617-4917 | - |
local.provider.type | wosris | - |
local.uhasselt.uhpub | yes | - |
local.uhasselt.international | yes | - |
item.validation | ecoom 2020 | - |
item.accessRights | Restricted Access | - |
item.fullcitation | Outay, Fatma; Taha, Bilal; Chaabani, Hazar; Kamoun, Faouzi; Werghi, Naoufel & YASAR, Ansar (2021) Estimating ambient visibility in the presence of fog: a deep convolutional neural network approach. In: PERSONAL AND UBIQUITOUS COMPUTING, 25, p. 51-62. | - |
item.fulltext | With Fulltext | - |
item.contributor | Outay, Fatma | - |
item.contributor | Taha, Bilal | - |
item.contributor | Chaabani, Hazar | - |
item.contributor | Kamoun, Faouzi | - |
item.contributor | Werghi, Naoufel | - |
item.contributor | YASAR, Ansar | - |
crisitem.journal.issn | 1617-4909 | - |
crisitem.journal.eissn | 1617-4917 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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s00779-019-01334-w.pdf Restricted Access | Published version | 1.95 MB | Adobe PDF | View/Open Request a copy |
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