Abstract
Driver’s drowsiness is considered as a major reason behind accidents on road, around the globe. Driving nonstop for a long period of time will cause accidents. The consequences of drowsy state are the same as alcohol, and it will create a driver’s driving inputs poorer, destroy the driver’s reaction times, and blur driver’s thought processes. To prevent such disastrous situations, a real-time driver monitoring system is implemented using OpenCV, where the aspect ratios of extracted contour features of eye and mouth are measured, and an alarm is generated. With EAR 0.25 and MAR 0.75, the results show that the alarm is generated for the blinks. The robustness of the implementation has been verified by changing the EAR and MAR, values, and best results are given for the EAR 0.25 and MAR 0.75.
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Sai Sandeep Raju, V.T., Belwal, M. (2021). Driver Drowsiness Detection. In: Smys, S., Palanisamy, R., Rocha, Á., Beligiannis, G.N. (eds) Computer Networks and Inventive Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 58. Springer, Singapore. https://doi.org/10.1007/978-981-15-9647-6_77
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DOI: https://doi.org/10.1007/978-981-15-9647-6_77
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