Research Article
Driver Drowsiness Detection and Notification Through Facial Pattern Analysis
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 185 - Issue 12 |
| Published: May 2023 |
| Authors: Bonaparte A. De Vega, Jairus S. Delos Reyes, Mateo Madrine F. Magalong, Michael M. Mislang, Jenniea A. Olalia |
10.5120/ijca2023922783
|
Bonaparte A. De Vega, Jairus S. Delos Reyes, Mateo Madrine F. Magalong, Michael M. Mislang, Jenniea A. Olalia . Driver Drowsiness Detection and Notification Through Facial Pattern Analysis. International Journal of Computer Applications. 185, 12 (May 2023), 6-10. DOI=10.5120/ijca2023922783
@article{ 10.5120/ijca2023922783,
author = { Bonaparte A. De Vega,Jairus S. Delos Reyes,Mateo Madrine F. Magalong,Michael M. Mislang,Jenniea A. Olalia },
title = { Driver Drowsiness Detection and Notification Through Facial Pattern Analysis },
journal = { International Journal of Computer Applications },
year = { 2023 },
volume = { 185 },
number = { 12 },
pages = { 6-10 },
doi = { 10.5120/ijca2023922783 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2023
%A Bonaparte A. De Vega
%A Jairus S. Delos Reyes
%A Mateo Madrine F. Magalong
%A Michael M. Mislang
%A Jenniea A. Olalia
%T Driver Drowsiness Detection and Notification Through Facial Pattern Analysis%T
%J International Journal of Computer Applications
%V 185
%N 12
%P 6-10
%R 10.5120/ijca2023922783
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Drowsy driving is one of the primary causes of road accidents. This typically occurs when a driver has not obtained adequate rest. It can also be caused by untreated sleep disorders, drugs, excessive alcohol consumption, or shift employment. From 63,072 in 2007 to 116,906 in 2018, the number of car accidents in the Philippines has more than doubled. Every year, 12,000 Filipinos are killed or injured in road accidents involving passengers, drivers, and pedestrians, with 14,553 people killed or injured [1]. The proponents developed a project that can monitor drowsiness and may mitigate untoward incidents resulting from it. The project is composed of two sub-systems: an image processing module and a wearable alert device. The proponents used facial pattern analysis to determine drowsiness using an advanced method of facial recognition. The proponents also developed a web application to help the operator monitor the drowsiness status of the driver and the geolocation of the truck. If the driver became drowsy, the web application would notify the operator of the status of the driver. The web application has a map function that tracks the driver’s location using a marker.
References
- Charise Kim, Car Accidents in The Philippines: Causes, Facts & Latest Statistics.
- MMDA, TEC- ROAD SAFETY UNIT MMARAS ANNUAL REPORT 2017.
- Kickstarter, STEER: Wearable Device That Will Not Let You Fall Asleep.
- Sadegh Arefnezhad 2020, Driver Drowsiness Classification Using Data Fusion of Vehicle-based Measures and ECG Signals.
- Pratiksha Kolpe 2020, Drowsiness Detection and Warning System Using Python.
- L.Thulasimani 2021, Real Time Driver Drowsiness Detection Using Opencv and Facial Landmarks.
- Achmad Mustofa Luthfi 2019, Google Maps API Implementation on IOT Platform for Tracking an Object Using GPS.
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