A learning system for detection and classification of road obstacles, such as vehicles and non-vehicles, is proposed which utilizes information from multiple sensors. An advanced range sensor guides a selection of candidate images provided by the camera for subsequent analysis. A competition based learning algorithm is used to distinguish between representations of different obstacles. High classification accuracy is demonstrated in a realistic variety of driving conditions in the presence of intentional data mislabeling in the two-class setup with state-of-art image descriptors.


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    Title :

    Road obstacle classification with attention windows


    Contributors:


    Publication date :

    2010-06-01


    Size :

    718750 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Road Obstacle Classification with Attention Windows, pp. 889-895

    Prokhorov, D. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2010



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