In this paper we describe a plate-recognition system for access control to restricted areas. The system we propose is based on vision, neural networks and a neuro-fuzzy system. Vision is used to detect the license-plate and to single out the characters it contains, while a neural network-based classifier is used to "read" the plate. The resulting string is then matched to a "white list" of allowed plates, to check the transit authorization. In case matching fails, a neuro-fuzzy agent analyses the features of the mismatch, to filter out possible false alarms and to optimize the workload for the human supervisor that has to validate the alarm.


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    Title :

    Access control system with neuro-fuzzy supervision


    Contributors:
    Adorni, G. (author) / Cagnoni, S. (author) / Gori, M. (author) / Mordonini, M. (author)


    Publication date :

    2001-01-01


    Size :

    689677 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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