Skip navigation
Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5593
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKumaran S.K.-
dc.contributor.authorMohapatra S.-
dc.contributor.authorDogra D.P.-
dc.contributor.authorPratim Roy, Partha-
dc.contributor.authorKim B.-G.-
dc.date.accessioned2020-10-06T15:56:51Z-
dc.date.available2020-10-06T15:56:51Z-
dc.date.issued2019-
dc.identifier.citationExpert Systems with Applications (2019), 134(): 267-278-
dc.identifier.issn9574174-
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2019.05.049-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/5593-
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.language.isoen_US-
dc.publisherElsevier Ltd-
dc.relation.ispartofExpert Systems with Applications-
dc.subjectDirichlet process mixture model-
dc.subjectIsolated intersections-
dc.subjectTraffic signal timing-
dc.subjectUnsupervised machine learning-
dc.subjectVisual surveillance-
dc.titleComputer vision-guided intelligent traffic signaling for isolated intersections-
dc.typeArticle-
dc.scopusid57209233557-
dc.scopusid57207740175-
dc.scopusid35408975400-
dc.scopusid56880478500-
dc.scopusid7501567302-
dc.affiliationKumaran, S.K., School of Electrical Science, Indian Institute of Technology Bhubaneswar, Bhubaneswar, 752050, India-
dc.affiliationMohapatra, S., School of Electrical Science, Indian Institute of Technology Bhubaneswar, Bhubaneswar, 752050, India-
dc.affiliationDogra, D.P., School of Electrical Science, Indian Institute of Technology Bhubaneswar, Bhubaneswar, 752050, India-
dc.affiliationRoy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India-
dc.affiliationKim, B.-G., Department of IT Engineering, Sookmyung Women's University Seoul, Seoul, 04310, South Korea-
dc.description.correspondingauthorKumaran, S.K.; School of Electrical Science, Indian Institute of Technology BhubaneswarIndia; email: sk47@iitbbs.ac.in-
Appears in Collections:Journal Publications [CS]

Files in This Item:
There are no files associated with this item.
Show simple item record


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