Abstract
Physical security and personal protection characteristics are developed to avoid lethargy. In the automotive industry, this is a significant technical challenge. Driving during grogginess is a key reason, particularly nowadays, behind road accidents. If drowsy, the risk of collapsing may be greater than in an alert state. There is therefore a important aid in preventing accidents using assistive technologies to monitor the driver’s alertness level. In this publication, the driver uses visual characteristics and intoxication identification with an alcohol sensor for the identification of insobriety. Driver bleakness also depends on the driver’s head, nose and nose classification in real-time driving either on a deserted road or a road full of traffic.
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Patil, A. (2020). Real Drowsiness Detection Using Viola–Jones Algorithm in Tensorflow. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_30
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DOI: https://doi.org/10.1007/978-981-15-1884-3_30
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