Bakheet Samy, Al-Hamadi Ayoub
Department of Information Technology, Faculty of Computers and Information, Sohag University, P. O. Box 82533 Sohag, Egypt.
Institute for Information Technology and Communications (IIKT), Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany.
Brain Sci. 2021 Feb 14;11(2):240. doi: 10.3390/brainsci11020240.
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.
由于其高度的独特性、对光照的鲁棒性以及简单的计算，方向梯度直方图（HOG）特征在许多计算机视觉任务中备受关注并取得了显著成功。本文提出了一种用于驾驶员困倦检测的创新框架，其中基于移位方向的二值化直方图，从HOG特征的改进版本形成了一种具有独特性、鲁棒性和紧凑性的自适应描述符。由二值化HOG特征生成的最终HOG描述符被输入到训练好的朴素贝叶斯（NB）分类器中，以做出最终的驾驶员困倦判定。在公开可用的NTHU-DDD数据集上的实验结果验证了所提出的框架有潜力成为几个最先进基线的有力竞争者，实现了85.62%的有竞争力的检测准确率，且没有损失效率或稳定性。
