본문 바로가기
  • Home

Lane Detection Using Biased Discriminant Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2017, 22(3), pp.27-34
  • DOI : 10.9708/jksci.2017.22.03.027
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 8, 2017
  • Accepted : March 5, 2017
  • Published : March 31, 2017

Tae Kyung Kim 1 Kwak, Nojun 2 Sang-Il Choi 1

1단국대학교
2서울대학교

Accredited

ABSTRACT

We propose a cascade lane detector that uses biased discriminant analysis (BDA) to work effectively even when there are various external factors on the road. The proposed cascade detector was designed with an existing lane detector and the detection module using BDA. By placing the BDA module in the latter stage of the cascade detector, the erroneously detected results by the existing detector due to sunlight or road fraction were filtered out at the final lane detection results. Experimental results on road images taken under various environmental conditions showed that the proposed method gave more robust lane detection results than conventional methods alone.

Citation status

* References for papers published after 2022 are currently being built.