Disclosures herein teach applying a set of sections spanning a down-sampled version of an image of a road-scene to a low-fidelity classifier to determine a set of candidate sections for depicting one or more objects in a set of classes. The set of candidate sections of the down-sampled version may be mapped to a set of potential sectors in a high-fidelity version of the image. A high-fidelity classifier may be used to vet the set of potential sectors, determining the presence of one or more objects from the set of classes. The low-fidelity classifier may include a first Convolution Neural Network (CNN) trained on a first training set of down-sampled versions of cropped images of objects in the set of classes. Similarly, the high-fidelity classifier may include a second CNN trained on a second training set of high-fidelity versions of cropped images of objects in the set of classes.


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    Titel :

    КЛАССИФИКАТОРЫ НИЗКОГО И ВЫСОКОГО КАЧЕСТВА, ПРИМЕНЯЕМЫЕ К ИЗОБРАЖЕНИЯМ ДОРОЖНЫХ СЦЕН


    Erscheinungsdatum :

    2018-07-18


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Russisch


    Klassifikation :

    IPC:    G06V / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS