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.
Road detection using support vector machine based on online learning and evaluation
2010 IEEE Intelligent Vehicles Symposium ; 256-261
2010-06-01
933147 byte
Conference paper
Electronic Resource
English
Road Detection Using Support Vector Machine Based on Online Learning and Evaluation, pp. 256-261
British Library Conference Proceedings | 2010
|Performance Evaluation of Color Based Road Detection Using Neural Nets and Support Vector Machines
British Library Conference Proceedings | 2004
|Online-Learning Support Vector Machine Approach for Short Term Load Forecasting
British Library Online Contents | 2005
|