Driver distraction and fatigue have become one of the leading causes of severe traffic accidents. Hence, the systems that implement driver monitoring systems are crucial. Usually such systems used a monocular camera to recognize driver behavior. Even with the growing development of advanced driver assistance systems and the introduction of third-level autonomous vehicles, this task is still trending and complex due to challenges such as in-cabin illumination change and the dynamic background. To reliably compare and validate driver inattention monitoring methods a limited number of public datasets are available. The paper proposes a methodology for in-the-wild dataset creation of vehicle driver for recording an oculomotor activity, a video images of a driver as well as relevant smartphone sensors that track vehicle movement. Based on the methodology we plan to conduct in-the-wild experiments.


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    Titel :

    Methodology for in-the-Wild Driver Monitoring Dataset Formation


    Beteiligte:


    Erscheinungsdatum :

    2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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