Cited time in webofscience Cited time in scopus

Full metadata record

DC Field Value Language
dc.contributor.author Lee, Chung-Hee -
dc.contributor.author Lim, Young-Chul -
dc.contributor.author Kim, Dongyoung -
dc.contributor.author Sohng, Kyu-Ik -
dc.date.available 2017-07-11T06:29:03Z -
dc.date.created 2017-04-10 -
dc.date.issued 2014-11 -
dc.identifier.issn 1392-1215 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/3147 -
dc.description.abstract Despite significant progress in vehicle detection over the last few decades, vehicle detection performance in heavy traffic is still inadequate. In this paper, we propose a new algorithm for vehicle detection in heavy traffic to improve detection performance. It uses two proposed segmentation methods, namely, the disparity map-based bird's-eye-view mapping segmentation method and the edge distance weighted conditional random field (CRF)-based segmentation method. Our experimental results show that the proposed algorithm outperforms conventional algorithms. The improvements in performance range from 10.8 % to 20.5 % increase in F-measure. -
dc.language English -
dc.publisher Kauno Technologijos Universitetas -
dc.title Improved Vehicle Detection Algorithm in Heavy Traffic for Intelligent Vehicle -
dc.type Article -
dc.identifier.doi 10.5755/j01.eee.20.9.3734 -
dc.identifier.scopusid 2-s2.0-84911437121 -
dc.identifier.bibliographicCitation Elektronika ir Elektrotechnika, v.20, no.9, pp.54 - 58 -
dc.description.isOpenAccess FALSE -
dc.subject.keywordAuthor Intelligent vehicle -
dc.subject.keywordAuthor image segmentation -
dc.subject.keywordAuthor object recognition -
dc.subject.keywordAuthor stereo image processing -
dc.citation.endPage 58 -
dc.citation.number 9 -
dc.citation.startPage 54 -
dc.citation.title Elektronika ir Elektrotechnika -
dc.citation.volume 20 -
Files in This Item:

There are no files associated with this item.

Appears in Collections:
Division of AI, Big data and Block chain 1. Journal Articles
Division of Automotive Technology 1. Journal Articles

qrcode

  • twitter
  • facebook
  • mendeley

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

BROWSE