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Drunk Driving and Drowsiness Detection Alert System

  • Vivek Nair
  • Nadir Charniya
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Advancement of safety features to avert drunk and drowsy driving has been one of the leading technical challenges in the automobile business. Especially in this modern age where people are under serious work pressure has led to higher crash rates. To prevent such accidents this paper discusses the use of nonintrusive techniques by using visual features to determine whether driver is driving in alert state. Drowsiness detection has been implemented using HAAR Cascade for face and eye closure detection and yawn detection implemented using Template matching in visual studio 2013. For drunk state detection, an alcohol sensor (MQ-3) has been implemented to avoid drunk driving. If the driver is found to be in drunk or drowsy condition, then an alarm would be generated and the driver being alerted using a buzzer and a vibrator that can be placed in the seatbelt or under driver seat thus preventing from mishaps taking place.

Keywords

Alcohol sensor Alert system Drowsy Drunk HAAR Cascade Template matching Visual features 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. I (Vivek Nair—Author) would like to clarify that the participant (driver) is myself and give consent to be used in the study.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.EXTCV.E.S. Institute of TechnologyMumbaiIndia

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