Detailed Information

Cited 0 time in webofscience Cited 9 time in scopus
Metadata Downloads

Computer vision-guided intelligent traffic signaling for isolated intersections

Full metadata record
DC FieldValueLanguage
dc.contributor.authorKumaran S.K.-
dc.contributor.authorMohapatra S.-
dc.contributor.authorDogra D.P.-
dc.contributor.authorRoy P.P.-
dc.contributor.authorKim B.-G.-
dc.date.available2021-02-22T05:45:26Z-
dc.date.issued2019-11-
dc.identifier.issn0957-4174-
dc.identifier.issn1873-6793-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/2751-
dc.description.abstractComputer vision-guided traffic management is an emerging area of research. Intelligent traffic signal control using computer vision is a less explored area of research. In this paper, we propose a new approach of traffic flow-based intelligent signal timing by temporally clustering optical flow features of moving vehicles using Temporal Unknown Incremental Clustering (TUIC) model. First, we propose a new inference scheme that works approximately 5-times faster as compared to the one originally proposed in TUIC in a dense traffic intersection. The new inference scheme can trace clusters representing moving objects that may be occluded while being tracked. Cluster counts of approach roads have been used for signal timing for traffic intersections. It is done by detecting cluster motion inside the regions-of-interest (ROI) marked at the entry and exit locations of intersection approaches. Departure rates are learned using Gaussian regression to parameterize traffic variations. Using the learned parameters as a function of cluster count, an adaptive signal timing algorithm, namely Throughput and Average Waiting Time Optimization (TAWTO) has been proposed. Experimental results reveal that the proposed method can achieve better average waiting time and throughput as compared to the state-of-the-art signal timing algorithms. We intend to publish two datasets as part of this work for enabling the research community to explore computer vision aided solutions for typical problems such as intelligent traffic controlling, violation detection in chaotic road intersections, etc. © 2019 Elsevier Ltd-
dc.format.extent12-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier Ltd-
dc.titleComputer vision-guided intelligent traffic signaling for isolated intersections-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1016/j.eswa.2019.05.049-
dc.identifier.scopusid2-s2.0-85066938646-
dc.identifier.wosid000475997000022-
dc.identifier.bibliographicCitationExpert Systems with Applications, v.134, pp 267 - 278-
dc.citation.titleExpert Systems with Applications-
dc.citation.volume134-
dc.citation.startPage267-
dc.citation.endPage278-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusComputer vision-
dc.subject.keywordPlusLearning systems-
dc.subject.keywordPlusStreet traffic control-
dc.subject.keywordPlusDirichlet process mixture model-
dc.subject.keywordPlusIsolated intersection-
dc.subject.keywordPlusTraffic signal timings-
dc.subject.keywordPlusUnsupervised machine learning-
dc.subject.keywordPlusVisual surveillance-
dc.subject.keywordPlusTraffic signals-
dc.subject.keywordAuthorDirichlet process mixture model-
dc.subject.keywordAuthorIsolated intersections-
dc.subject.keywordAuthorTraffic signal timing-
dc.subject.keywordAuthorUnsupervised machine learning-
dc.subject.keywordAuthorVisual surveillance-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/abs/pii/S0957417419303847?via%3Dihub-
Files in This Item
Go to Link
Appears in
Collections
ETC > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kim, Byung Gyu photo

Kim, Byung Gyu
공과대학 (인공지능공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE