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    Using facial landmarks to detect driver fatigue

    Using facial landmarks to detect driver fatigue


    Using state-of-the-art computer vision techniques, a model was designed to be able to detect fatigue in haul truck drivers in South American mines. The proposed solution uses a DNN-based (Deep Neural Networks) face detection model, caffe, to detect the presence of the driver’s face and a facial landmark detection model of MobileNetV2 architecture to identify key features of the face. Afterwards, the eye aspect ratio (EAR) and the mouth aspect ratio (MAR) are calculated, which are then used to calculate the percent eye-closure over a time period (PERCLOS), blink rates and duration, yawns, distraction, and other indicators. The results successfully detect fatigue symptoms over 72% of the times on haul truck drivers in Peruvian mines.

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    Titel :

    Using facial landmarks to detect driver fatigue

    Beteiligte:
    Martinez, A. (Autor:in) / Berrospi, F. (Autor:in) / Porras, V. (Autor:in) / Portocarrero, M. (Autor:in)
    Erschienen in:

    2022 IEEE XXIX International Conference on Electronics, Electrical Engineering and Computing (INTERCON) ; 1-4

    Erscheinungsdatum :

    2022-08-11

    Format / Umfang :

    9973435 byte

    ISBN :

    978-1-6654-8636-1 , 978-1-6654-8635-4

    DOI :

    https://doi.org/10.1109/INTERCON55795.2022.9870046

    Medientyp :

    Aufsatz (Konferenz)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

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