Improved Vehicle Detection Algorithm in Heavy Traffic for Intelligent Vehicle

  • Chung-Hee Lee Daegu Gyeongbuk Institute of Science & Technology(DGIST)
  • Young-Chul Lim Daegu Gyeongbuk Institute of Science & Technology(DGIST)
  • Dongyoung Kim Daegu Gyeongbuk Institute of Science & Technology(DGIST)
  • Kyu-Ik Sohng Kyungpook National University
Keywords: Intelligent vehicle, image segmentation, object recognition, stereo image processing

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.

DOI: http://dx.doi.org/10.5755/j01.eee.20.9.3734

Published
2014-11-05
How to Cite
Lee, C.-H., Lim, Y.-C., Kim, D., & Sohng, K.-I. (2014). Improved Vehicle Detection Algorithm in Heavy Traffic for Intelligent Vehicle. Elektronika Ir Elektrotechnika, 20(9), 54-58. https://doi.org/10.5755/j01.eee.20.9.3734
Section
SYSTEM ENGINEERING, COMPUTER TECHNOLOGY