Real-time image segmentation and rule-based reasoning for vehicle head light detection on a moving vehicle

Bing-Fei Wu*, Yen Lin Chen, Yuan Hsin Chen, Chao Jung Chen, Chuan Tsai Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

This study proposes a vehicle detection system for identifying the vehicles by locating their headlights and rear-lights in the nighttime road environment. The proposed system comprises of two stages for detecting the vehicles in front of the camera-assisted car. The first stage is a fast automatic multilevel thresholding, which separates the bright objects from the grabbed nighttime road scene images. This proposed automatic multilevel thresholding approach provide the robustness and adaptability for the system to operate on various illuminated conditions at night. Then the extracted bright objects are processed by the second stage - the proposed knowledge-based connected-component analysis procedure, to identify the vehicles by locating their vehicle lights, and estimate the distance between the camera-assisted car and the detected vehicles. Experimental results demonstrate the feasibility and effectiveness of the proposed approach on vehicle detection at night.

Original languageEnglish
Title of host publicationProceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005
EditorsM.W. Marcellin
Pages388-393
Number of pages6
StatePublished - 1 Dec 2005
EventSeventh IASTED International Conference on Signal and Image Processing, SIP 2005 - Honolulu, HI, United States
Duration: 15 Aug 200517 Aug 2005

Publication series

NameProceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005

Conference

ConferenceSeventh IASTED International Conference on Signal and Image Processing, SIP 2005
Country/TerritoryUnited States
CityHonolulu, HI
Period15/08/0517/08/05

Keywords

  • Autonomous vehicles
  • Image segmentation
  • Multilevel thresholding
  • Night scene
  • Vehicle detection

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