A comparative analysis on driver drowsiness detection using CNN

Naren Thiruvalar, V.; Vimal, E.

doi:10.22075/ijnaa.2021.5894

A comparative analysis on driver drowsiness detection using CNN

Document Type : Research Paper

Authors

V. Naren Thiruvalar

E. Vimal

Department of Instrumentation and Control Systems Engineering, PSG College of Technology, Coimbatore, India

10.22075/ijnaa.2021.5894

Abstract

The main objective of this project is to detect driver’s drowsiness and alert the driver which is an important precautionary measure in order to avoid accidents. Here two different algorithms based on Convolution Neural Network (CNN) were applied and the results were compared respectively. “Highway Hypnosis” is a serious issue to be addressed while driving especially on highways. Drivers who travel on highways continuously for more than 3 hours must be aware of this serious problem. If there is proper knowledge of it, fatalities would be drastically reduced. In this project, a dedicated detection coupled with an alarm system is provided to alert the driver in case of drowsiness. CNN is used since it is very effective in analyzing images and videos. In this project, a live video feed is used to detect drowsiness by suitable algorithms.

Keywords

CNN

Drowsiness Detection

Viola-Jones

PERCLOS

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