A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification
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 p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/11/2/240 |
| _version_ | 1797396494355005440 |
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| author | Samy Bakheet Ayoub Al-Hamadi |
| author_facet | Samy Bakheet Ayoub Al-Hamadi |
| author_sort | Samy Bakheet |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:52:06Z |
| format | Article |
| id | doaj.art-00e4defb8ed5409ab0a7696b4b61c71f |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-09T00:52:06Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-00e4defb8ed5409ab0a7696b4b61c71f2023-12-11T17:05:51ZengMDPI AGBrain Sciences2076-34252021-02-0111224010.3390/brainsci11020240A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian ClassificationSamy Bakheet0Ayoub Al-Hamadi1Department of Information Technology, Faculty of Computers and Information, Sohag University, P. O. Box 82533 Sohag, EgyptInstitute for Information Technology and Communications (IIKT), Otto-von-Guericke University Magdeburg, 39106 Magdeburg, GermanyDue 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.https://www.mdpi.com/2076-3425/11/2/240driver drowsiness detectionHOG featuresshifted orientationsNB classificationNTHU-DDD dataset |
| spellingShingle | Samy Bakheet Ayoub Al-Hamadi A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification Brain Sciences driver drowsiness detection HOG features shifted orientations NB classification NTHU-DDD dataset |
| title | A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification |
| title_full | A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification |
| title_fullStr | A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification |
| title_full_unstemmed | A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification |
| title_short | A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification |
| title_sort | framework for instantaneous driver drowsiness detection based on improved hog features and naive bayesian classification |
| topic | driver drowsiness detection HOG features shifted orientations NB classification NTHU-DDD dataset |
| url | https://www.mdpi.com/2076-3425/11/2/240 |
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