Robust lane detection for video-based navigation systems

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

doi:10.1109/ICTAI.2007.20

Robust lane detection for video-based navigation systems

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

School of Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 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 language	English
Title of host publication	Proceedings 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007
Pages	535-538
Number of pages	4
DOIs	https://doi.org/10.1109/ICTAI.2007.20
Publication status	Published - 2007
Event	19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007 - Patras, Greece Duration: 2007 Oct 29 → 2007 Oct 31

Publication series

Name	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume	2
ISSN (Print)	1082-3409

Other

Other	19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007
Country/Territory	Greece
City	Patras
Period	07/10/29 → 07/10/31

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/ICTAI.2007.20

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). Article 4410435 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2). https://doi.org/10.1109/ICTAI.2007.20

Robust lane detection for video-based navigation systems. / Kim, Sunghoon; Park, Jeong Ho; Cho, Seong Ik et al.
Proceedings 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007. 2007. p. 535-538 4410435 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, S, Park, JH, Cho, SI, 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., 4410435, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, vol. 2, pp. 535-538, 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007, Patras, Greece, 07/10/29. https://doi.org/10.1109/ICTAI.2007.20

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title = "Robust lane detection for video-based navigation systems",

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.",

author = "Sunghoon Kim and Park, {Jeong Ho} and Cho, {Seong Ik} and Soonyoung Park and Kisung Lee and Kyoungho Choi",

year = "2007",

doi = "10.1109/ICTAI.2007.20",

language = "English",

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AB - 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.

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Robust lane detection for video-based navigation systems

Abstract

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