Efficient lane detection based on spatiotemporal images

Soonhong Jung, Junsic Youn, Sanghoon Sull

Research output: Contribution to journalArticle

70 Citations (Scopus)

Abstract

In this paper, we propose an efficient method for reliably detecting road lanes based on spatiotemporal images. In an aligned spatiotemporal image generated by accumulating the pixels on a scanline along the time axis and aligning consecutive scanlines, the trajectory of the lane points appears smooth and forms a straight line. The aligned spatiotemporal image is binarized, and two dominant parallel straight lines resulting from the temporal consistency of lane width on a given scanline are detected using a Hough transform, reducing alignment errors. The left and right lane points are then detected near the intersections of the straight lines and the current scanline. Our spatiotemporal domain approach is more robust missing or occluded lanes than existing frame-based approaches. Furthermore, the experimental results show not only computation times reduced to as little as one-third but also a slightly improved rate of detection.

Original languageEnglish
Article number7217838
Pages (from-to)289-295
Number of pages7
JournalIEEE Transactions on Intelligent Transportation Systems
Volume17
Issue number1
DOIs
Publication statusPublished - 2016 Jan 1

Keywords

  • Alignment
  • Binarization
  • Cubic model
  • Hough transform
  • Lane detection
  • Lane tracking
  • Scanline
  • Spatiotemporal image
  • Temporal consistency

ASJC Scopus subject areas

  • Automotive Engineering
  • Computer Science Applications
  • Mechanical Engineering

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