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A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian

Samy Bakheet^1,2, Ayoub Al-Hamadi²

¹Department of Information Technology, Faculty of Computers and Information, Sohag University, P. O. Box 82533 Sohag, Egypt.

|March 6, 2021

Summary

This summary is machine-generated.

This study introduces an improved Histogram of Oriented Gradient (HOG) method for driver drowsiness detection. The novel framework achieves 85.62% accuracy, offering an efficient and stable solution.

Keywords:

HOG features NB classification NTHU-DDD dataset driver drowsiness detection shifted orientations

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Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Histogram of Oriented Gradient (HOG) features are widely used in computer vision due to their distinctiveness and robustness.
Driver drowsiness is a significant safety concern, necessitating reliable detection methods.

Purpose of the Study:

To propose an innovative framework for driver drowsiness detection using an improved HOG feature descriptor.
To enhance the distinctiveness, robustness, and compactness of HOG features for this specific application.

Main Methods:

An adaptive descriptor was developed from an improved HOG feature based on binarized histograms of shifted orientations.
The generated descriptor was fed into a trained Naïve Bayes (NB) classifier for drowsiness determination.

Main Results:

The proposed framework achieved a competitive detection accuracy of 85.62% on the NTHU-DDD dataset.
The method demonstrated efficiency and stability, comparable to state-of-the-art baselines.

Conclusions:

The developed framework shows potential as a strong contender for driver drowsiness detection systems.
The improved HOG features offer a promising approach for enhancing the accuracy and reliability of drowsiness detection.