• AHSAN SHARIFF M
• Dr. NELSON KENNEDY BABU C

The main reason for motor vehicle accidents is the driver's drowsiness. In this project, the presence of drowsiness will detect and alert the driver. For the detection of drowsiness, the most relevant visual indications are the driver condition such as the behavior of the eyes. The Algorithm used in this project is opencv, HaarCascade with machine learning. This system works adequately under natural lighting conditions and no matter the use of driver accessories like glasses or a cap, etc. With the concept of machine learning and it's techniques the driver will be alerted and a consent message will be sent to a family member or a friend. Due to a large number of traffic accidents when driver has fallen asleep, this proposal was developed in order to prevent these kinds of accidents and without the necessity of purchasing any specialized devices.

Haar-Cascade, Machine Learning, openCV

"Monitoring Driver Behavior using Machine Learning Techniques – A Comprehensive approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 11, page no.c619-c622, November-2021, Available :http://www.jetir.org/papers/JETIR2111283.pdf

"Monitoring Driver Behavior using Machine Learning Techniques – A Comprehensive approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 11, page no. ppc619-c622, November-2021, Available at : http://www.jetir.org/papers/JETIR2111283.pdf