DocumentCode :
2259472
Title :
Vision-Based Road Detection by Adaptive Region Segmentation and Edge Constraint
Author :
Wang, Yanqing ; Chen, Deyun ; Shi, Chaoxia
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Univ. of Sci. & Technol., Harbin
Volume :
1
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
342
Lastpage :
346
Abstract :
A novel vision-based road detection method was proposed in this paper to realize visual guiding navigation for ground mobile vehicles in outdoor environments. The road region was first segmented from the jumbled backgrounds 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 by reducing the unnecessary disturbances from the useless edge existed in background By combining the detection algorithm of both road region and road boundary, the road detection method proposed in this paper was robust against strong shadows, surface dilapidation and illumination variations. It has been tested on real ground mobile vehicle and performed well in real road environments.
Keywords :
automated highways; computer vision; edge detection; filtering theory; image motion analysis; image segmentation; road vehicles; traffic engineering computing; Canny edge extraction; OTSUadaptive threshold segmentation algorithm; adaptive region segmentation; edge constraint; grey image; ground mobile vehicle; intelligent transport system; realize visual guiding navigation; road region filtering; vision-based road detection method; Detection algorithms; Image edge detection; Image recognition; Image segmentation; Land vehicles; Lighting; Navigation; Road vehicles; Robustness; Vehicle detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3497-8
Type :
conf
DOI :
10.1109/IITA.2008.203
Filename :
4739591
Link To Document :
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