DocumentCode :
887008
Title :
SCARF: a color vision system that tracks roads and intersections
Author :
Crisman, Jill D. ; Thorpe, Charles E.
Author_Institution :
Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA
Volume :
9
Issue :
1
fYear :
1993
fDate :
2/1/1993 12:00:00 AM
Firstpage :
49
Lastpage :
58
Abstract :
SCARF, a color vision system that recognizes difficult roads and intersections, is presented. It has been integrated into several navigation systems that drive a robot vehicle, the Navlab, on a variety of roads in many different weather conditions. SCARF recognizes roads that have degraded surfaces and edges with no lane markings in difficult shadow conditions. It also recognizes intersections with or without predictions from the navigation system. This is the first system that detects intersections in images without a priori knowledge of the intersection shape and location. SCARF uses Bayesian classification to determine a road-surface likelihood for each pixel in a reduced color image. It then evaluates a number of road and intersection candidates by matching an ideal road-surface likelihood image with the results from the Bayesian classification. The best matching candidate is passed to a path-planning system that navigates the robot vehicle on the road or intersection. The SCARF system is described in detail, results on a variety of images are presented, and Navlab test runs using SCARF are discussed
Keywords :
Bayes methods; computer vision; computerised navigation; image recognition; mobile robots; path planning; Bayesian classification; Navlab; SCARF; color vision system; computer vision; image recognition; mobile robots; navigation; path-planning system; road tracking; Bayesian methods; Degradation; Image edge detection; Machine vision; Navigation; Road vehicles; Robots; Shape; Vehicle driving; Weather forecasting;
fLanguage :
English
Journal_Title :
Robotics and Automation, IEEE Transactions on
Publisher :
ieee
ISSN :
1042-296X
Type :
jour
DOI :
10.1109/70.210794
Filename :
210794
Link To Document :
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