DocumentCode :
3529247
Title :
Road detection using support vector machine based on online learning and evaluation
Author :
Zhou, Shengyan ; Gong, Jianwei ; Xiong, Guangming ; Chen, Huiyan ; Iagnemma, Karl
Author_Institution :
Intell. Vehicle Res. Center, Beijing Inst. of Technol., Beijing, China
fYear :
2010
fDate :
21-24 June 2010
Firstpage :
256
Lastpage :
261
Abstract :
Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying road and non-road classes and improves the adaptability of the road detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based road detection algorithm on intelligent vehicle.
Keywords :
driver information systems; feature extraction; image classification; object detection; support vector machines; vehicles; SVM; autonomous self-guided vehicles; driver assistance systems; feature extraction; front-view road detection; intelligent vehicle; road detection algorithm; self-supervised online learning; support vector machine; training data; Detection algorithms; Feature extraction; Machine learning; Mobile robots; Remotely operated vehicles; Road vehicles; Support vector machine classification; Support vector machines; Vehicle detection; Vehicle driving;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2010 IEEE
Conference_Location :
San Diego, CA
ISSN :
1931-0587
Print_ISBN :
978-1-4244-7866-8
Type :
conf
DOI :
10.1109/IVS.2010.5548086
Filename :
5548086
Link To Document :
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