ArticleJournal of imaging2023
Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Emotion-Aware Contextual Modelling for Robust Driver Fatigue Detection.Sensors (Basel, Switzerland) · 2026Article
- Domain-robust vision transformer with hierarchical swin encoding for explainable low-latency driver drowsiness detection.Scientific reports · 2026Article
- Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications.Scientific reports · 2026Article
- A fatigue driving detection method based on driver posture and facial state analysis.Scientific reports · 2026Article
- Quantifying the visual salience of Swedish vowel contrasts: a computer vision approach.Frontiers in psychology · 2026Article
- Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach.Scientific reports · 2025Article
- Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.Sensors (Basel, Switzerland) · 2024Article
- Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drowsiness-related car accidents continue to have a significant effect on road safety. Many of these accidents can be eliminated by alerting the drivers once they start feeling drowsy. This work presents a non-invasive system for real-time driver drowsiness detection using visual features. These features are extracted from videos obtained from a camera installed on the dashboard. The proposed system uses facial landmarks and face mesh detectors to locate the regions of interest where mouth aspect ratio, eye aspect ratio, and head pose features are extracted and fed to three different classifiers: random forest, sequential neural network, and linear support vector machine classifiers. Evaluations of the proposed system over the National Tsing Hua University driver drowsiness detection dataset showed that it can successfully detect and alarm drowsy drivers with an accuracy up to 99%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.