Optimized Real Time Drowsy Driver Warning System Using Raspberry Pi

  • A. Almah RoshniEmail author
  • J. Balaji
  • D. Kaushik
  • K. R. Sarath Chandran
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 44)


Accidents are undesirable, incidental and unexpected events that can be prevented if they are recognized and acted upon, prior to their occurrence. Driver drowsiness is one of the major causes of road accidents. A solution to this problem is inclusion of a drowsiness warning system in vehicles to warn the driver of drowsiness. The main objective of this paper is to develop a non-intrusive real-time drowsiness warning system by monitoring eye state of the driver. The proposed system uses eye-aspect-ratio and is implemented in Raspberry Pi. This system is optimized using three techniques, by re-sizing the frame to be processed, using a single eye for drowsy detection and by not processing every frame. The selection of frames is based on analyzing the redundancy nature of video frames. This system is designed by considering several factors to minimize frame processing latency to match with Raspberry Pi’s processing speed.


Drowsy driver Driver state Haar cascade classifier Facial landmark detection Eye aspect ratio Raspberry Pi 



All author states that there is conflict of interest.

We used author photo in the Figs. 1 and 2.

We used our own data.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • A. Almah Roshni
    • 1
    Email author
  • J. Balaji
    • 1
  • D. Kaushik
    • 1
  • K. R. Sarath Chandran
    • 1
  1. 1.Department of Computer Science EngineeringSSN College of EngineeringChennaiIndia

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