Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking
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%.
Abdulwahed, M.N., 2018. Analysis of image noise reduction using neural network. Engineering and Technology Journal, 36, pp.76-87.
Abed, I.S., 2019. Lung cancer detection from X-ray images by combined Backpropagation neural network and PCA. Engineering and Technology Journal, 37, pp.166-171.
Alshaqaqi, B., Baquhaizel, A.S., Ouis, M.E.A., Boumehed, M., Ouamri, A. and Keche, M., 2013. Driver drowsiness detection system. In: 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA), pp.151-155.
Bagci, A.M., Ansari, R., Khokhar, A. and Cetin, E., 2004. Eye tracking using Markov models. In: Proceedings of the 17th International Conference on Pattern Recognition, IEEE, pp.818-821.
Bamidele, A., Kamardin, K., Syazarin, N., Mohd, S., Shafi, I., Azizan, A., Aini, N. and Mad, H., 2019. Non-intrusive driver drowsiness detection based on face and eye tracking. International Journal of Advanced Computer Science and Applications, 10, pp.549-569.
Bati, A.F. and Adam, N.E., 2006. Hybrid neuro-genetic based controller of power system. Iraqi Journal of Computers, Communication, Control and Systems Engineering, 6, pp.112-125.
Chen, J., Wang, H. and Hua, C. 2018. Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks. International Journal of Psychophysiology, 133, pp.120-130.
Computer Vision Lab, 2016. Driver Drowsiness Detection Dataset. Available from: http://cv.cs.nthu.edu.tw/php/callforpaper/datasets/DDD [Last accessed on 2016 Nov 12].
Costa, M., 2019. Detecting driver’s fatigue, distraction and activity using a nonintrusive ai-based monitoring system. Journal of Artificial Intelligence and Soft Computing Research, 9, pp.247-266.
De Naurois, C.J., Bourdin, C., Stratulat, A., Diaz, E. and Vercher, J.L., 2019. Detection and prediction of driver drowsiness using artificial neural network models. Accident Analysis and Prevention, 126, pp.95-104.
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K. and Darrell, T., 2015, Long-term recurrent convolutional networks for visual recognition and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp.2625-2634.
Dreißig, M., Baccour, M.H., Schäck, T. and Kasneci, E., 2020. Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm. In: IEEE Symposium Series on Computational Intelligence (SSCI). p889-896.
Ghourabi, A., Ghazouani, H. and Barhoumi, W., 2020. Driver drowsiness detection based on joint monitoring of yawning, blinking and nodding. In: IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP). p407-414.
Gwak, J., Hirao, A. and Shino, M., 2020. An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing. Applied Sciences, 10, p.2890.
Hassan, A.K. and Mohammed, S.N., 2020. A novel facial emotion recognition scheme based on graph mining. Defence Technology, 16, pp.1062-1072.
Hassan, A.K.A. and Jasim, S.S., 2010. Integrating neural network with genetic algorithms for the classification plant disease. Engineering and Technology Journal, 28, pp.686-702.
Heidari, A.A. and Pahlavani, P., 2017. An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Applied Soft Computing, 60, pp.115-134.
Hong, K., Min, J., Lee, W. and Kim, J., 2005. Real time face detection and recognition system using Haar-like feature/HMM in ubiquitous network environments. In: International Conference on Computational Science and its Applications, Springer, Berlin. p1154-1161.
Huang, X., Cheng, C. and Zhang, X.B., 2021. Machine learning and numerical investigation on drag reduction of underwater serial multi-projectiles. Defense Technology, 18, pp.229-237.
Islam, M.R., Matin, A. and Kamruzzaman, T., 2020. Automatic Identification of Driver Inattentiveness Using Convolutional Neural Networks. In: IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). pp.21-24.
Kadhm, M.S. and Hassan, A.K.A., 2015. Handwriting word recognition based on SVM classifier. International Journal of Advanced Computer Science and Applications, 1, pp.64-68.
Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, pp.1097-1105.
Kumar, A. and Patra, R., 2018. Driver drowsiness monitoring system using visual behaviour and machine learning. In: 2018 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), IEEE. pp.339-344.
Lawoyin, S., Liu, X., Fei, D.Y. and Bai, O., 2014. Detection methods for a low-cost accelerometer-based approach for driver drowsiness detection. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE. pp. 1636-1641.
Liu, T., Xie, J., Yan, W. and Li, P., 2012. Driver’s face detection using spacetime restrained adaboost method. KSII Transactions on Internet and Information Systems (TIIS), 6, pp.2341-2350.
Mehta, S., Dadhich, S., Gumber, S. and Jadhav Bhatt, A., 2019a. Real-time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio. In: Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Amity University Rajasthan, Jaipur, India.
Mehta, S., Mishra, P., Bhatt, A.J. and Agarwal, P., 2019. AD3S: Advanced driver drowsiness detection system using machine learning. In: 5th International Conference on Image Information Processing (ICIIP), IEEE. pp. 108-113.
Nwobi-Okoye, C.C. and Ochieze, B.Q., 2018. Age hardening process modeling and optimization of aluminum alloy A356/Cow horn particulate composite for brake drum application using RSM, ANN and simulated annealing. Defense Technology, 14, pp.336-345.
Park, S., Pan, F., Kang, S. and Yoo, C.D., 2016. Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks. In: Asian Conference on Computer Vision. Springer, Berlin. pp. 154-164.
Parkhi, O.M., Vedaldi, A. and Zisserman, A., 2015. Deep Face Recognition. Rashid, T.A. and Abdullah, S.M., 2018. Ahybrid of artificial bee colony, genetic algorithm, and neural network for diabetic mellitus diagnosing. ARO The Scientific Journal of Koya University, 6, pp.55-64.
Ravi Teja, P., Anjana Gowri, G., Preethi Lalithya, G., Ajay, R., Anuradha, T. and Kumar, P., 2021. Driver drowsiness detection using convolution neural networks. In: Smart Computing Techniques and Applications. Springer, Berlin.
Rowley, H.A., Baluja, S. and Kanade, T., 1998. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, pp.23-38.
Vu, T.H., Dang, A. and Wang, J.C., 2019. A deep neural network for real-time driver drowsiness detection. IEICE Transactions on Information and Systems, 102, pp.2637-2641.
Wang, Q., Yang, J., Ren, M. and Zheng, Y., 2006. Driver Fatigue Detection: A Survey. 6th World Congress on Intelligent Control and Automation.
Weng, C.H., Lai, Y.H. and Lai, S.H., 2016. Driver drowsiness detection via a hierarchical temporal deep belief network. In: Asian Conference on Computer Vision. Springer, Berlin. pp. 117-133.
Xu, L., Wang, H., Lin, W., Gulliver, T.A. and Le, K.N., 2019. GWO-BP neural network based OP performance prediction for mobile multiuser communication networks. IEEE Access, 7, pp.152690-152700.
Yu, J., Park, S., Lee, S. and Jeon, M., 2016. Representation learning, scene understanding, and feature fusion for drowsiness detection. In: Asian Conference on Computer Vision, Springer, Berlin. pp. 165-177.
Yu, J., Park, S., Lee, S. and Jeon, M., 2018. Driver drowsiness detection using condition-adaptive representation learning framework. IEEE Transactions on Intelligent Transportation Systems, 20, pp.4206-4218.
Yu, X., Wang, S.H. and Zhang, Y.D., 2021. CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia. Information Processing and Management, 58, p.102411.
Yusiong, J.P.T., 2012. Optimizing artificial neural networks using cat swarm optimization algorithm. International Journal of Intelligent Systems and Applications, 5, p.69.
Zhang, F., Su, J., Geng, L. and Xiao, Z., 2017. Driver fatigue detection based on eye state recognition. In: International Conference on Machine Vision and Information Technology (CMVIT), IEEE. pp. 105-110.
Zhang, W., Cheng, B. and Lin, Y., 2012. Driver drowsiness recognition based on computer vision technology. Tsinghua Science and Technology, 17, pp.354-362.
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