Application of convolutional neural networks for visibility estimation of CCTV images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Giyenko, A. | - |
dc.contributor.author | Palvanov, A. | - |
dc.contributor.author | Cho, Y. | - |
dc.date.available | 2020-02-27T12:43:29Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 1976-7684 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4355 | - |
dc.description.abstract | In this paper we discuss the possibility of application of a Convolutional Neural Network for visual atmospheric visibility estimation. A system utilizing such a neural network can greatly benefit a smart city by providing real time localized visibility data across all highways and roads by utilizing a dense network of traffic and security cameras that exist in most developed urban areas. To achieve this, we implemented a Convolutional Neural Network with 3 convolution layers and trained it on a data set taken from CCTV cameras in South Korea. This approach allowed us achieve accuracy above 84%. In the paper we describe the network structure and training process, as well as some final thoughts on the next steps in our research. © 2018 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.relation.isPartOf | International Conference on Information Networking | - |
dc.subject | Cameras | - |
dc.subject | Deep learning | - |
dc.subject | Learning systems | - |
dc.subject | Neural networks | - |
dc.subject | Smart city | - |
dc.subject | Visibility | - |
dc.subject | Atmospheric visibility | - |
dc.subject | Convolutional neural lietivorks | - |
dc.subject | Convolutional neural network | - |
dc.subject | Highways and roads | - |
dc.subject | Network structures | - |
dc.subject | Security cameras | - |
dc.subject | Training process | - |
dc.subject | Visibility estimation | - |
dc.subject | Convolution | - |
dc.title | Application of convolutional neural networks for visibility estimation of CCTV images | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1109/ICOIN.2018.8343247 | - |
dc.identifier.bibliographicCitation | International Conference on Information Networking, v.2018-January, pp.875 - 879 | - |
dc.identifier.scopusid | 2-s2.0-85046995649 | - |
dc.citation.endPage | 879 | - |
dc.citation.startPage | 875 | - |
dc.citation.title | International Conference on Information Networking | - |
dc.citation.volume | 2018-January | - |
dc.contributor.affiliatedAuthor | Giyenko, A. | - |
dc.contributor.affiliatedAuthor | Palvanov, A. | - |
dc.contributor.affiliatedAuthor | Cho, Y. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Atmospheric visibility | - |
dc.subject.keywordAuthor | Convolutional neural lietivorks | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Smart city | - |
dc.subject.keywordPlus | Cameras | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Smart city | - |
dc.subject.keywordPlus | Visibility | - |
dc.subject.keywordPlus | Atmospheric visibility | - |
dc.subject.keywordPlus | Convolutional neural lietivorks | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Highways and roads | - |
dc.subject.keywordPlus | Network structures | - |
dc.subject.keywordPlus | Security cameras | - |
dc.subject.keywordPlus | Training process | - |
dc.subject.keywordPlus | Visibility estimation | - |
dc.subject.keywordPlus | Convolution | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.