DocumentCode :
2724415
Title :
Extracting road curvature and orientation from image edge points without perceptual grouping into features
Author :
Kluge, Karl
Author_Institution :
Artificial Intelligence Lab., Michigan Univ., Ann Arbor, MI, USA
fYear :
1994
fDate :
24-26 Oct. 1994
Firstpage :
109
Lastpage :
114
Abstract :
The ARCADE (Automated Road Curvature And Direction Estimation) algorithm estimates road curvature and tangential road orientation relative to the camera line-of-sight. The input to ARCADE consists of edge point locations and orientations extracted from an image, and it performs the estimation without the need for any prior perceptual grouping of the edge points into individual lane boundaries. It is able to achieve this through the use of global constraints on the individual lane boundary shapes derived from an explicit model of road structure in the world. The use of the least median squares robust estimation technique allows the algorithm to function correctly in cases where up to 50% of the input edge data points are contaminating noise. Two applications of ARCADE as the first stage of processing for a lane sensing task are described: 1) the extraction of the locations of the features defining the visible lane structure of the road; and 2) the generation of training instances for an ALVINN-like neural network road follower.
Keywords :
computer vision; edge detection; feature extraction; navigation; road vehicles; ARCADE; direction estimation; feature extraction; global constraints; image edge points; lane boundary; least median squares; neural network road follower; road curvature estimation; road curvature extraction; road vehicle; tangential road orientation; Cameras; Humans; Iron; Layout; Neural networks; Noise robustness; Noise shaping; Roads; Shape; Signal generators;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles '94 Symposium, Proceedings of the
Print_ISBN :
0-7803-2135-9
Type :
conf
DOI :
10.1109/IVS.1994.639482
Filename :
639482
Link To Document :
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