Please use this identifier to cite or link to this item:
Title: Driver fatigue detection from facial features
Authors: Wang, Ping
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: P1010-191
Abstract: Road traffic injury brings great harm and economic loss to individuals, families, and society. Fatigue driving is one of the causes of traffic accidents. Prevention is the fundamental strategy to prevent and reduce traffic accidents. In this project, a driver fatigue detection system is programmed by using python, OpenCV and Keras. When the system detects the driver feeling sleepy, it will send out an alarm to remind the driver to stop for a little adjustment or rest, which can effectively prevent and reduce the occurrence of traffic accidents and provide a strong guarantee for the safety of people's lives and property. In the driver fatigue driving detection system, Harr cascade classifier corner detection method is used to determine the specific position of the face and eyes and the driver's eyes feature extraction. By using OpenCV to collect images from webcam, and then input them into CNN model to classified whether the human eyes are open or closed. Warning is given according to the duration of eye closure to remind drivers to pay attention to safety. The chapter 5 will explain and elaborate the proposed algorithm in detail.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
P1010-191 FYP FINAL REPORT_WANG PING (U1620458B) .pdf
  Restricted Access
3.83 MBAdobe PDFView/Open

Page view(s)

Updated on Jan 29, 2023

Download(s) 50

Updated on Jan 29, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.