[{“box”:0,”content”:”[if 992 equals=”Open Access”]
Open Access
n
[/if 992]n
n
n
n
n
- n t
n
Md Moniruzzaman Piyash, Nitin Puli, Shivam Kumar, Karandeep Kaur
[/foreach]
n
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Student, Student, Student, Assistant Professor, School of Computer Science and Engineering, Lovely Professional University, School of Computer Science and Engineering, Lovely Professional University, School of Computer Science and Engineering, Lovely Professional University, School of Computer Science and Engineering, Lovely Professional University,, Punjab, Punjab, Punjab, Punjab, India, India, India, India
n[/if 1175][/foreach]
[/if 2099][if 2099 equals=”Yes”][/if 2099]nn
Abstract
nDRIVE-DEFENDER presents a pioneering approach in driver safety through the development of a real-time machine learning system for alcohol detection. The detrimental impact of alcohol-impaired driving on road safety necessitates efficient detection mechanisms. Current methodologies are often hindered by their cost, invasiveness, and reliance on specialized sensors. In response, DRIVE- DEFENDER utilizes a for recording the face of the driver in real time on camera, employing image processing techniques to identify facial landmarks. These landmarks serve as inputs for the calculation of features indicative of alcohol impairment, such as changes in facial expression and coordination. Machine learning algorithms, coupled with adaptive thresholding mechanisms, enable accurate detection of alcohol intoxication without the need for expensive hardware or causing distractions to the driver. The proposed approach offers a cost-effective, non-intrusive solution for enhancing driving safety in real-time by promptly identifying and mitigating the risks associated with alcohol-impaired driving.
n
Keywords: fatigue, safety of drivers, aspect ratios for the eyes and mouth, head pose estimation, computer vision, image processing, Detection of alcohol consumption, drowsiness detection.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Open Source Developments(joosd)]
n
n
n
n
n
n
n[if 992 equals=”Open Access”] Full Text PDF Download[else] nvar fieldValue = “[user_role]”;nif (fieldValue == ‘indexingbodies’) {n document.write(‘Full Text PDF‘);n }nelse if (fieldValue == ‘administrator’) { document.write(‘Full Text PDF‘); }nelse if (fieldValue == ‘joosd’) { document.write(‘Full Text PDF‘); }n else { document.write(‘ ‘); }n [/if 992] [if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | April 5, 2024 | |
| Accepted | April 8, 2024 | |
| Published | April 10, 2024 |
n
n
n
n
n
nn function myFunction2() {n var x = document.getElementById(“browsefigure”);n if (x.style.display === “block”) {n x.style.display = “none”;n }n else { x.style.display = “Block”; }n }n document.querySelector(“.prevBtn”).addEventListener(“click”, () => {n changeSlides(-1);n });n document.querySelector(“.nextBtn”).addEventListener(“click”, () => {n changeSlides(1);n });n var slideIndex = 1;n showSlides(slideIndex);n function changeSlides(n) {n showSlides((slideIndex += n));n }n function currentSlide(n) {n showSlides((slideIndex = n));n }n function showSlides(n) {n var i;n var slides = document.getElementsByClassName(“Slide”);n var dots = document.getElementsByClassName(“Navdot”);n if (n > slides.length) { slideIndex = 1; }n if (n (item.style.display = “none”));n Array.from(dots).forEach(n item => (item.className = item.className.replace(” selected”, “”))n );n slides[slideIndex – 1].style.display = “block”;n dots[slideIndex – 1].className += ” selected”;n }n”}]