AI Article Synopsis
- HOG features are effective for computer vision due to their distinctiveness, robustness to lighting changes, and ease of computation.
- A new method for detecting driver drowsiness is introduced, utilizing an improved version of HOG features based on binarized histograms for better performance.
- The approach shows promising results with an 85.62% accuracy rate in driver drowsiness detection, outperforming many existing methods while maintaining efficiency.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7917813 | PMC |
| http://dx.doi.org/10.3390/brainsci11020240 | DOI Listing |
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