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Proceedings Paper

Vision-based road detection by hidden Markov model
Author(s): Yanqing Wang; Deyun Chen; Liyuan Tao; Chaoxia Shi
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Paper Abstract

A novel vision-based road detection method was proposed in this paper to realize visual guiding navigation for ground mobile vehicles (GMV). The original image captured by single camera was first segmented into the road region and nonroad region by using an adaptive threshold segmentation algorithm named OTSU. Subsequently, the Canny edges extracted in grey images would be filtered in the road region so that the road boundary could be recognized accurately among those disturbances caused by other edges existed in the image. In order to improve the performance of road detection, the dynamics of GMV and the Hidden Markov Model (HMM) was taken into account to associate the possible road boundary at different time step. The method proposed in this paper was robust against strong shadows, surface dilapidation and illumination variations. It has been tested on real GMV and performed well in real road environments.

Paper Details

Date Published: 10 July 2009
PDF: 8 pages
Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890Y (10 July 2009); doi: 10.1117/12.836839
Show Author Affiliations
Yanqing Wang, Harbin Univ. of Science and Technology (China)
Deyun Chen, Harbin Univ. of Science and Technology (China)
Liyuan Tao, Harbin Univ. of Science and Technology (China)
Chaoxia Shi, Nanjing Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 7489:
PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)

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