IIUM Repository

Computer vision based driver assistance drowsiness detection

Emashharawi, Maryam J. S. and Khalifa, Othman Omran and Abdul Malik, Noreha (2020) Computer vision based driver assistance drowsiness detection. In: The 12th National Technical Seminar on Unmanned System Technology 2020 (NUSYS’20), 24th- 25th November 2020, Kuala Lumpur. (Unpublished)

[img] PDF - Submitted Version
Restricted to Registered users only

Download (858kB) | Request a copy
[img] PDF - Supplemental Material
Restricted to Registered users only

Download (357kB) | Request a copy


Nowadays, drowsiness is a serious cause of traffic accidents, a problem of major concern to society. Driver fatigue or sleepiness decreases the driver’s reaction time, reduces attention, and affects the quality of decision making which impairs the driving experience. Therefore, in this paper, a drowsiness detection system is designed based on computer vision, using a cascade of classifiers based on Haar-like features. The system is able to detect the face and eyes of the driver and determine the eyes closure or opening, which concludes the downiness of the driver. The paper presents the five primary steps involves which are: video acquirement, frame separation, face detection, eyes detection and drowsiness detection.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 4119/85622
Uncontrolled Keywords: Eyes detection, face detection, eye tracking, driver behaviour, driver assistance system.
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Prof. Dr Othman O. Khalifa
Date Deposited: 04 Dec 2020 16:20
Last Modified: 04 Dec 2020 16:21
URI: http://irep.iium.edu.my/id/eprint/85622

Actions (login required)

View Item View Item


Downloads per month over past year