Robust lane detection for video-based navigation systems

Sunghoon Kim, Jeong Ho Park, Seong Ik Cho, Soonyoung Park, Kisung Lee, Kyoungho Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Lane detection from a live video captured in a moving vehicle is an important issue for autonomous vehicles and video-based navigation systems. In this paper, we present a novel idea for robust lane detection and lane color recognition. More specifically, a framework for robust lane detection is presented. Then, a novel idea to reduce illumination effects is presented. Lastly, SVM approach is presented to recognize lane color robustly for various lighting conditions including shadow, backlight, sunset, and so on. By combining information from navigation database, it is possible to decide if we are in the leftmost, middle, or the rightmost lane, which allows us to provide more realistic navigation information to drivers. Simulation results are provided to show the robustness of the proposed idea.

Original languageEnglish
Title of host publicationProceedings 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007
Pages535-538
Number of pages4
DOIs
Publication statusPublished - 2007
Event19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007 - Patras, Greece
Duration: 2007 Oct 292007 Oct 31

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2
ISSN (Print)1082-3409

Other

Other19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007
CountryGreece
CityPatras
Period07/10/2907/10/31

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Kim, S., Park, J. H., Cho, S. I., Park, S., Lee, K., & Choi, K. (2007). Robust lane detection for video-based navigation systems. In Proceedings 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007 (pp. 535-538). [4410435] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2). https://doi.org/10.1109/ICTAI.2007.20