For various applications it is useful to automatically divide vehicles passing a point into several categories. Forth te method described the sampled signal of an inductive loop detector, called the footprint, is used. This signal is normalized to become the input of neural network that is used to separate the passing vehicles into five categories. This paper describes the choices that were made in the selection of the footprint transformation and the selection of the neural network to be used. A set of vehicle measurements with an accompanying video tape was used to train the network and to test the performance of a trained network. This research leads to the conclusion that the developed classification system, based on a 3-layer perceptron with backpropagation, gives useable results. Further research is necessary to investigate the possibilities of other types of networks and the influence of footprint transformation methods.


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    Title :

    Classification of vehicles using neural networks


    Additional title:

    Die Einstufung von Fahrzeugen durch die Benutzung neuronaler Netzwerke


    Contributors:


    Publication date :

    1993


    Size :

    22 Seiten, 9 Bilder, 3 Tabellen, 7 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Classification of vehicles using neural networks

    Reijmers,J.J. / Univ.of Technol.,Delft,NL | Automotive engineering | 1993


    Classification of vehicles using neural networks

    Reijmers, J. J. / European Automobile Engineers Cooperation | British Library Conference Proceedings | 1993




    Neural networks for intelligent vehicles

    Pomerleau, D.A. | Tema Archive | 1993