Traffic light detection using rotated principal component analysis for video-based car navigation system

Sung-Kwan Joo, Yongkwon Kim, Seong Ik Cho, Kyoungho Choi, Kisung Lee

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This letter presents a novel approach for traffic light detection in a video frame captured by an in-vehicle camera. The algorithm consists of rotated principal component analysis (RPCA), modified amplitude thresholding with respect to the histograms of the PC planes and final filtering with a neural network. The proposed algorithm achieves an average detection rate of 96% and is very robust to variations in the image quality.

Original languageEnglish
Pages (from-to)2884-2887
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE91-D
Issue number12
DOIs
Publication statusPublished - 2008 Dec 1

Keywords

  • Car navigation system
  • Crossroad detection
  • Principal component analysis
  • Traffic light

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Software
  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition

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