DocumentCode :
3224246
Title :
A Synchronous Detection of the Road Boundary and Lane Marking for Intelligent Vehicles
Author :
Lu, Weina ; Wang, Haifang ; Wang, Qingzhu
Author_Institution :
Hebei Normal Univ. of Sci. & Technol., Qinhuangdao
Volume :
1
fYear :
2007
fDate :
July 30 2007-Aug. 1 2007
Firstpage :
741
Lastpage :
745
Abstract :
To prevent an intelligent vehicle from departing the lane in the vision-based navigation, an integrated method based on image processing is proposed to detect the road boundary and lane marking synchronously in structural road environment. The feature of the road boundary is extracted by means of gradient magnitude and gradient direction of pixels. And the lane marking feature is extracted by self-adaptive threshold segmenting with region connectivity analyzing. The characteristic points of both the road boundary and lane marking are matched to the straight or crooked road models by least-squares fit. With the circular calling of detecting and tracking blocks for mass image sequences, the whole process shows a real time and high antinoise capability. All the algorithms in the paper have been tested by the videos captured from real road scenes, and the experimental results proved that the detecting method is efficient, stable and accurate.
Keywords :
automated highways; computer vision; feature extraction; image recognition; image segmentation; image sequences; object detection; road vehicles; antinoise capability; image matching; image processing; image sequences; intelligent vehicle; lane marking feature extraction; road boundary detection; self-adaptive threshold segmentation; vision-based navigation; Feature extraction; Image processing; Image segmentation; Image sequences; Intelligent vehicles; Navigation; Roads; Testing; Vehicle detection; Videos;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-2909-7
Type :
conf
DOI :
10.1109/SNPD.2007.82
Filename :
4287602
Link To Document :
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