Vehicle counting, time-of-travel analysis, and other traffic studies frequently require the classification and identification of vehicles in a roadway. Unfortunately, many current technologies for identifying vehicles, such as image-based methods that use cameras and machine vision, are not appropriate for studies that require low-power consumption and low cost. Additionally, privacy issues are becoming a larger concern with the increasing controversy surrounding the public collection of imagery. In this work we evaluate a multi-modal sensing approach to vehicle classification and identification using an ensemble of sensors measurements including electromagnetic emanations and acoustic signatures. A novel kernel regression method is also used for signal learning to classify and identify vehicles without the need of invasive images. Multi-mode sensing, as well as signal learning, is shown to significantly increase the classification rate of specific vehicle classes.


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    Titel :

    Vehicle Classification and Identification Using Multi-Modal Sensing and Signal Learning


    Beteiligte:
    Kerekes, Ryan A. (Autor:in) / Karnowski, Thomas P. (Autor:in) / Kuhn, Mike (Autor:in) / Moore, Michael R. (Autor:in) / Stinson, Brad (Autor:in) / Tokola, Ryan (Autor:in) / Anderson, Adam (Autor:in) / Vann, Jason M. (Autor:in)


    Erscheinungsdatum :

    2017-06-01


    Format / Umfang :

    245171 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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