Abstract
Detect distracted driver is an essential factor to maintain road safety and avoid the risk of accidents and deaths. Studies of the World Health Organization shows that the distraction caused by mobile phones can increase the crash risk by up to 400%. This paper proposes a convolutional neural network that is able to monitor drivers video surveillance, more specifically detect and classify when the driver is using a cell phone. The experiments show an impressive accuracy, achieving up 99% of accuracy detecting distracted driver.
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Torres, R., Ohashi, O., Carvalho, E., Pessin, G. (2017). A Deep Learning Approach to Detect Distracted Drivers Using a Mobile Phone. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_9
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DOI: https://doi.org/10.1007/978-3-319-68612-7_9
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