Open AccessA Novel Approach to Detect Driver Drowsiness and Alcohol Intoxication using Haar Algorithm with Raspberry Pi
Author(s) -
M. Agna Manu,
Dayana Jaijan,
S. N. Nissa,
S. Jesna,
Abin Shukoor,
A. R. Shamna
Publication year - 2020
Publication title -
international journal of research in engineering, science and management
Language(s) - English
Resource type - Journals
ISSN - 2581-5792
DOI - 10.47607/ijresm.2020.284
Subject(s) - drunk driving , face detection , raspberry pi , alcohol intoxication , warning system , computer science , driving under the influence , artificial intelligence , computer vision , simulation , real time computing , poison control , computer security , pattern recognition (psychology) , human factors and ergonomics , injury prevention , medicine , medical emergency , facial recognition system , telecommunications , internet of things
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.