International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 6
November-December 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Driver Drowsiness Detection using Machine Learning Approach

Author(s) Vikash Gupta
Country India
Abstract Driver fatigue is a major cause of traffic accidents. The number of deaths and injuries increases every year around the world. Traffic accidents can be reduced by detecting driver fatigue. This article describes machine learning for sleep detection. Face detection is used to detect the driver's eye area and use this as a reference for eye tracking in subsequent frames. Finally, visual images are used to detect sleep and a warning system is created. This method is divided into three stages: face detection, eye detection, and fatigue detection. Image processing is used to recognize the driver's face and then extract the image of the driver's eyes to detect fatigue. HAAR face detection algorithm outputs the image and then adjusts face detection based on the output. CHT is then used to track the eyes of the visible face. Check the eyes using EAR (Early Evaluation). The proposed system was tested using the proposed system on a Raspberry pi 3 Model B with 1 GB RAM using Logitech HD Webcam C270. According to some video tests, average eye contact and tracking accuracy can reach 95.0%. Therefore, it is a cheaper and better solution for a tired driver to ask to find the road immediately.
Keywords Haar Face detection, AdaBoost, EAR(EyeAspectRatio), Raspberrypi3
Field Computer
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-20
Cite This Driver Drowsiness Detection using Machine Learning Approach - Vikash Gupta - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.12245
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.12245
Short DOI https://doi.org/gtfmqw
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com