Driver drowsiness detection using Gray Wolf Optimizer based on face and eye tracking

Journal article


Jasim, S., Abdul Hassan, AK and Turner, S. 2022. Driver drowsiness detection using Gray Wolf Optimizer based on face and eye tracking. Aro - The Scientific Journal of Koya University. 10 (1), pp. 49-56. https://doi.org/10.14500/aro.10928
AuthorsJasim, S., Abdul Hassan, AK and Turner, S.
Abstract

It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%.

KeywordsArtificial neural network; Drowsiness; Feature extraction; Gray wolf optimizer; Normalization; Segmentation
Year2022
JournalAro - The Scientific Journal of Koya University
Journal citation10 (1), pp. 49-56
PublisherKoya University
ISSN2307-549X
Digital Object Identifier (DOI)https://doi.org/10.14500/aro.10928
Official URLhttps://aro.koyauniversity.org/index.php/aro/article/view/928
Publication dates
Print12 May 2022
Publication process dates
Accepted12 May 2022
Deposited13 May 2022
Publisher's version
License
Output statusPublished
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