Work2013

Driver drowsiness detection system

Belal Alshaqaqi · Abdullah Salem Baquhaizel · Mohamed El Amine Ouis · Meriem Boumehed · Abdelaziz Ouamri · Mokhtar Keche

133 citations0/0 files storedInternational Workshop on Systems, Signal Processing and their Applications

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Sleep and Work-Related Fatigue Human-Automation Interaction and Safety Face recognition and analysis

Abstract

Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Every year, they increase the amounts of deaths and fatalities injuries globally. In this paper, a module for Advanced Driver Assistance System (ADAS) is presented to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety; this system deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence. We propose an algorithm to locate, track, and analyze both the drivers face and eyes to measure PERCLOS, a scientifically supported measure of drowsiness associated with slow eye closure.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 133 citations exist upstream.

Cited by

2018Driver drowsiness monitoring system using visual behaviour and machine learning 2019Real Time Driver Fatigue Detection Based on Facial Behaviour along with Machine Learning Approaches 2022Computer vision-based approach to detect fatigue driving and face mask for edge computing device

Imported from OpenAlex (CC0) — nothing here is generated.