Existing methodologies to count vehicles from a road image have depended upon both hand-crafted feature engineering and rule-based algorithms. These require many predefined thresholds to detect and track vehicles. This paper provides a supervised learning methodology that requires no such feature engineering. A deep convolutional neural network was devised to count the number of vehicles on a road segment based solely on video images. The present methodology does not regard an individual vehicle as an object to be detected separately; rather, it collectively counts the number of vehicles as a human would. The test results show that the proposed methodology outperforms existing schemes.


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    Titel :

    Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network


    Beteiligte:
    Chung, Jiyong (Autor:in) / Sohn, Keemin (Autor:in)


    Erscheinungsdatum :

    2018-05-01


    Format / Umfang :

    1535578 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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