Image-Based Learning to Measure the Space Mean Speed on a Stretch of Road without the Need to Tag Images with Labels
DC Field | Value | Language |
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dc.contributor.author | Lee, Jincheol | - |
dc.contributor.author | Roh, Seungbin | - |
dc.contributor.author | Shin, Johyun | - |
dc.contributor.author | Sohn, Keemin | - |
dc.date.available | 2019-05-28T03:34:00Z | - |
dc.date.issued | 2019-03 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/18519 | - |
dc.description.abstract | Space mean speed cannot be directly measured in the field, although it is a basic parameter that is used to evaluate traffic conditions. An end-to-end convolutional neural network (CNN) was adopted to measure the space mean speed based solely on two consecutive road images. However, tagging images with labels (=true space mean speeds) by manually positioning and tracking every vehicle on road images is a formidable task. The present study was focused on naïve animation images provided by a traffic simulator, because these contain perfect information concerning vehicle movement to attain labels. The animation images, however, seem far-removed from actual photos taken in the field. A cycle-consistent adversarial network (CycleGAN) bridged the reality gap by mapping the animation images into seemingly realistic images that could not be distinguished from real photos. A CNN model trained on the synthesized images was tested on real photos that had been manually labeled. The test performance was comparable to those of state-of-the-art motion-capture technologies. The proposed method showed that deep-learning models to measure the space mean speed could be trained without the need for time-consuming manual annotation. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | NLM (Medline) | - |
dc.title | Image-Based Learning to Measure the Space Mean Speed on a Stretch of Road without the Need to Tag Images with Labels | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/s19051227 | - |
dc.identifier.bibliographicCitation | Sensors (Basel, Switzerland), v.19, no.5 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000462540400246 | - |
dc.identifier.scopusid | 2-s2.0-85062834617 | - |
dc.citation.number | 5 | - |
dc.citation.title | Sensors (Basel, Switzerland) | - |
dc.citation.volume | 19 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | space mean speed | - |
dc.subject.keywordAuthor | convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | cycle-consistent adversarial network (CycleGAN) | - |
dc.subject.keywordAuthor | traffic surveillance | - |
dc.subject.keywordAuthor | traffic prediction | - |
dc.subject.keywordPlus | TRACKING | - |
dc.subject.keywordPlus | LOOP | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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