DocumentCode :
3308897
Title :
A vision-based road edge detection algorithm
Author :
Wang, Rongben ; Xu, Youchun ; Libin ; Zhao, Yufan
Author_Institution :
Transp. of Coll., Jilin Univ., Changchun, China
Volume :
1
fYear :
2002
fDate :
17-21 June 2002
Firstpage :
141
Abstract :
In the paper, a road edge identification algorithm is developed. The new idea of this method is to use natural road edge, as well as the white strip for road information acquisition. The natural road edge does not be easily polluted like the white lane maker does, so it indicates better adaptability to the outdoor environment. In the algorithm, we use both the pixel feature and the frame feature to identify the road edge, which is referred to as the whole road model. Because several road constrains is used to ensure the road edge detection, the algorithm is immune to the influence of the image disturbance. The algorithm of the road edge identification includes two stages: initialization detection and tracing detection. The initialization stage detects the road edge from the whole road image. The trace algorithm uses the region of interest (ROI) to limit detecting area, which can save much time. In order to give a measure of the reliability of the road detecting result, this paper presents a road edge identification estimation function, which can estimate the reliability of the road edge.
Keywords :
edge detection; mobile robots; reliability; road vehicles; robot vision; detecting area limitation; image disturbance; initialization detection; reliability estimation; road edge identification algorithm; road edge identification estimation function; road information acquisition; trace algorithm; tracing detection; vision-based road edge detection algorithm; white strip; Deformable models; Educational institutions; Image edge detection; Intelligent vehicles; Machine vision; Millimeter wave radar; Radar imaging; Roads; Shape; Transportation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicle Symposium, 2002. IEEE
Print_ISBN :
0-7803-7346-4
Type :
conf
DOI :
10.1109/IVS.2002.1187942
Filename :
1187942
Link To Document :
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