A vision-based road detection technique is important for implementation of a safe driving assistance system. A major problem of vision-based road detection is sensitivity to environmental change, especially illumination change. A novel framework is proposed for robust road detection using a color model with a separable brightness component. Road candidate areas are selected using an adaptive thresholding method, then fast region merging is performed based on a threshold value. Extracted road contours are filtered using edge information. Experimental results show the proposed algorithm is robust in an illumination change environment.

This work was supported by the Sun Moon University Research Grant of 2014.

Authors and Affiliations

1. Department of Computer Engineering, Sun Moon University, Asan, Korea

   Jae-Hyun Nam, Seung-Hoon Yang, Woong Hu & Byung-Gyu Kim

Corresponding author

Correspondence toByung-Gyu Kim.

Editors and Affiliations

1. University of Nevada, Reno, Nevada, USA

   George Bebis

2. NASA Ames Research Center, Moffett Field, California, USA

   Richard Boyle

3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

   Bahram Parvin

4. Desert Research Institute, Reno, Nevada, USA

   Darko Koracin

5. University of Houston, Houston, Texas, USA

   Ioannis Pavlidis

6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

   Rogerio Feris

7. Purdue University, West Lafayette, Indiana, USA

   Tim McGraw

8. Side Effects Software, Santa Monica, California, USA

   Mark Elendt

9. The DiVE, Durham, North Carolina, USA

   Regis Kopper

10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

11. Kent State University, Kent, Ohio, USA

    Zhao Ye

12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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