A Novel Approach to Detect Driver Drowsiness and Alcohol Intoxication using Haar Algorithm with Raspberry Pi

M Agna Manu,S Jesna,A R Shamna,Dayana Jaijan,Abin Shukoor,S N Nissa

doi:10.47607/ijresm.2020.284

Abstract

Drowsiness in driver and alcohol consumption are the critical cause of road accident and death. Lives of pedestrian and passengers are put to risk as drivers tend to fall asleep and also when the driver is in his drunken state. Detection of driver drowsiness and its indication is an active research area now. There are 3 methods for detection of driver fatigue which includes vehicle-based method, behavioural method, and physiological based method. We adopt behavioural method. This project is aimed towards developing a prototype of drowsiness and alcohol detection system using Haar algorithm with raspberry pi. This project proposes a real time detection of driver’s drowsiness as well as alcohol intoxication and subsequently alerting them. The primary purpose of this drowsiness and alcohol detection system is to develop a system that can reduce the number of accidents from drowsiness and drunk driving of vehicle. It consists of camera which is placed in front of the driver to detect the face. An alcohol sensor which is a gas sensor used to sense the drinking state of driver. Haar algorithm is used for face detection. The results demonstrate the accuracy and robustness of the hybridized of image processing technique. Thus, it can be concluded the proposed approach is an effective solution for a real-time of driver drowsiness and alcohol detection.

Highlights

Driver fatigue and alcohol intoxication are the most significant factors in a large number of car accidents and death
There are about 1,50,000 road accidence in the India alone is due to the alcohol consumption or driver drowsiness
This type measures biological factor such as eyes movement, and here we adopt behavioural method which depend upon facial expressions such as state of eye whether the eye is closed or open. These techniques are generally characterized with the best detection accuracy. This type benefit from the dynamic behaviour of the human face and eye since they have a high degree of variability, face detection is considered to be a difficult problem in computer vision research, whereas the eyes can be considered salient and relatively stable feature on the face in comparison with other facial features

Summary

Introduction

Driver fatigue and alcohol intoxication are the most significant factors in a large number of car accidents and death. This type measures vehicle behaviours such as speed and position of the vehicle These techniques may be considered as non-intrusively but they have multiple limitations such as the type of the car, the driving conditions, geometry of the road steering pattern i.e. each driver’s has different style of driving and the geometry of the road is not uniform it varies, so here is a chance to create a false alarm. This type measures biological factor such as eyes movement, and here we adopt behavioural method which depend upon facial expressions such as state of eye whether the eye is closed or open These techniques are generally characterized with the best detection accuracy. Khushaba et proposed a method that detected the face using the facial features such as jaw contour, lip journals.resaim.com/ijresm | ISSN (Online): 2581-5792 | RESAIM Publishing contour and the shape of the entire face and the eyes were located and tracked using the Eigen eye method and the eye status is detected using correlation and edge detection methods

Objectives

Methods

Results