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.


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    Titel :

    Road detection using support vector machine based on online learning and evaluation


    Beteiligte:
    Shengyan Zhou, (Autor:in) / Jianwei Gong, (Autor:in) / Guangming Xiong, (Autor:in) / Huiyan Chen, (Autor:in) / Iagnemma, K (Autor:in)


    Erscheinungsdatum :

    01.06.2010


    Format / Umfang :

    933147 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Road Detection Using Support Vector Machine Based on Online Learning and Evaluation, pp. 256-261

    Zhou, S. / Gong, J. / Xiong, G. et al. | British Library Conference Proceedings | 2010


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