Unstructured road detection using hybrid features

Road detection is a key step of the autonomous guided vehicle system such as road following. In this paper, a novel unstructured road detection method is proposed. First, white balance and gray level stretch technique are adopted to enhance image performance. Then, a small overlapped sliding window...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 1; pp. 482 - 486
Main Authors Jian Wang, Zhong Ji, Yu-Ting Su
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Autonomous guided vehicle
Computer vision
Cybernetics
Feature extraction
Hybrid features
Machine learning
Mobile robots
Remotely operated vehicles
Roads
Shape
Support vector machines
SVM
Unstructured road detection
Vehicle detection
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Summary:Road detection is a key step of the autonomous guided vehicle system such as road following. In this paper, a novel unstructured road detection method is proposed. First, white balance and gray level stretch technique are adopted to enhance image performance. Then, a small overlapped sliding window is scanned over the frame from which hybrid features are extracted. Next, a SVM-based classifier is employed to distinguish the road area from background. At last, the morphological operation and moving average filter technology are performed to obtain precise location of the road region. The proposed algorithm has been evaluated by different type of unstructured roads and the experimental results show its effectiveness.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212506