The method comprises first converting an RGB frame 18 to an HSV frame which is then filtered by at least one threshold value to obtain at least one saturation frame 66. A contour is then extracted from the at least one saturation frame. Accordingly, a first portion of the RGB frame is cropped in order to encompass an area including the at least one contour. The first portion is then classified, for example to determine whether it corresponds to a not-a-traffic-light class, or a red/yellow/green­-traffic-light class 10, or the like. The classification could be performed by an artificial neural network or by using affinity scores. The method could be applied to controlling a vehicle or informing the driver of a vehicle (12) from which images can be captured by sensors (16). The efficiency of the classification process is improved by classifying only a cropped region of interest.


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    Titel :

    Traffic light detection and classification using computer vision and deep learning


    Beteiligte:
    MARYAM MOOSAEI (Autor:in) / MADELINE J GOH (Autor:in) / VIDYA NARIYAMBUT MURALI (Autor:in) / YI ZHANG (Autor:in)

    Erscheinungsdatum :

    2018-07-25


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

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




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