ArticleBrain sciences2021
A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Adaptive Multi-Temporal Fusion and Cross-Modal Adversarial Alignment for Robust Driver Fatigue Detection.Sensors (Basel, Switzerland) · 2026Article
- Article
- Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.Sensors (Basel, Switzerland) · 2024Article
- DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- A deep neural framework for real-time vehicular accident detection based on motion temporal templates.Heliyon · 2022Article
- Machine Learning-Based Intelligent Scoring of College English Teaching in the Field of Natural Language Processing.Computational intelligence and neuroscience · 2022Article
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Authors and funding
2 authors.
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Abstract
Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.
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