Vision-based road region detection using probability map of color features of road

Jae Hyun Lim, Jun Ho Chung, Tae Koo Kang, Myo Taeg Lim

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

1 Citation (Scopus)

Abstract

This paper proposes a road region detection algorithm using a vision sensor. The proposed algorithm uses color features of road to detect the road region. We first segment the image into patches to reduce the computational complexity and the noise. Superpixel method is applied to the segmentation instead of square patches for the precise segmentation. After the segmentation, similarities of patches are calculated by undirected graph-based shortest path algorithm. The distance between neighboring patches is defined as the Euclidian distance of CIE-Lab color and Illumination-invariant color. By using the similarity of patches, isolated region of the image and similarity of patches with bottom of the images are obtained. To ensure robust performance even when the non-road region is located at the bottom of the image, a probability map is constructed by combining isolated region of the image and similarity of patches with bottom of the image. Experimental results show the robustness of the proposed algorithm in various conditions.

Original languageEnglish
Title of host publicationICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
PublisherIEEE Computer Society
Pages53-55
Number of pages3
Volume2017-October
ISBN (Electronic)9788993215137
DOIs
Publication statusPublished - 2017 Dec 13
Event17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of
Duration: 2017 Oct 182017 Oct 21

Other

Other17th International Conference on Control, Automation and Systems, ICCAS 2017
CountryKorea, Republic of
CityJeju
Period17/10/1817/10/21

Fingerprint

Color
Computational complexity
Lighting
Sensors

Keywords

  • Autonomous driving
  • Probability map
  • Road region detection
  • Similarity of patches
  • Superpixel

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Lim, J. H., Chung, J. H., Kang, T. K., & Lim, M. T. (2017). Vision-based road region detection using probability map of color features of road. In ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings (Vol. 2017-October, pp. 53-55). IEEE Computer Society. https://doi.org/10.23919/ICCAS.2017.8204422

Vision-based road region detection using probability map of color features of road. / Lim, Jae Hyun; Chung, Jun Ho; Kang, Tae Koo; Lim, Myo Taeg.

ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. Vol. 2017-October IEEE Computer Society, 2017. p. 53-55.

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

Lim, JH, Chung, JH, Kang, TK & Lim, MT 2017, Vision-based road region detection using probability map of color features of road. in ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. vol. 2017-October, IEEE Computer Society, pp. 53-55, 17th International Conference on Control, Automation and Systems, ICCAS 2017, Jeju, Korea, Republic of, 17/10/18. https://doi.org/10.23919/ICCAS.2017.8204422
Lim JH, Chung JH, Kang TK, Lim MT. Vision-based road region detection using probability map of color features of road. In ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. Vol. 2017-October. IEEE Computer Society. 2017. p. 53-55 https://doi.org/10.23919/ICCAS.2017.8204422
Lim, Jae Hyun ; Chung, Jun Ho ; Kang, Tae Koo ; Lim, Myo Taeg. / Vision-based road region detection using probability map of color features of road. ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. Vol. 2017-October IEEE Computer Society, 2017. pp. 53-55
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