Abstract
Driver drowsiness has been observed as one of the most common causes for road accidents, producing nearly 40% of death and casualties. When a driver falls asleep, he starts losing control and is unable to take reflex action to avoid the accident or to reduce its impact. This necessitates the need for developing a mechanism that provides timely alerts to the driver when he is drowsy. In this paper, an efficient and non-intrusive algorithm that uses a deep convolutional neural network to analyze yawn behavior is proposed. The proposed technique is built by modifying the VGG16 architecture to include batch normalization, ReLu activation for the intermediate layers and sigmoid activation after the final dense layer. The performance of the proposed approach is verified on the YawDD dataset and is compared against VGG16, VGG19, MobileNet, and AlexNet. Experimental results show that the proposed approach outperforms the other networks in terms of accuracy.
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References
Qian, D., Wang, B., Qing, X., Zhang, T., Zhang, Y., Wang, X., Nakamura, M.: Drowsiness detection by Bayesian-Copula discriminant classifier based on EEG signals during daytime short nap. IEEE Trans. Biomed. Eng. 64(4), 743–754 (2017)
Gao, Z., Wang, X., Yang, Y., Mu, C., Cai, Q., Dang, W., Zuo, S.: EEG-based SpatioTemporal convolutional neural network for driver fatigue evaluation. IEEE Trans. Neural Netw. Learn. Syst. (early access) (2019)
Monkaresi, H., Bosch, N., Calvo, R.A., DMello, S.K.: Automated detection of engagement using video-based estimation of facial expressions and heart rate. IEEE Trans. Affect. Comput. 8(1), 15–28 (2017)
Chavarriaga, R., Uscumlic, M., Zhang, H., Khaliliardali, Z., Aydarkhanov, R., Saeedi, S., Gheorghe, L., Millan, J.D.R.: Decoding neural correlates of cognitive states to enhance driving experience. IEEE Trans. Emerg. Top. Comput. Intell. 2(4), 288–297 (2018)
Wang, W., Zhao, D., Han, W., Xi, J.: A learning-based approach for lane departure warning systems with a personalized driver model. IEEE Trans. Veh. Technol. 67(10), 9145–9157 (2018)
Soldera, J., Schu, G., Schardosim, L.R., Beltro, E.T.: Facial biometrics and applications. IEEE Instrum. Meas. Mag. 20(2), 4–10 (2017)
Yuen, K., Trivedi, M.M.: An occluded stacked hourglass approach to facial landmark localization and occlusion estimation. IEEE Trans. Intell. Veh. 2(4), 321–331 (2017)
Kar, A., Corcoran, P.: A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms. IEEE Access 5, 16495–16519 (2017)
Jeong, M., Ko, B.C., Kwak, S., Nam, J.-Y.: Driver facial landmark detection in real driving situations. IEEE Trans. Circ. Syst. Video Technol. 28(10), 2753–2767 (2018)
Yang, M., Crenshaw, J., Augustine, B., Mareachen, R., Wu, Y.: AdaBoost-based face detection for embedded systems. Comput. Vis. Image Underst. 114(11), 1116–1125 (2010)
Bouvier, C., Benoit, A., Caplier, A., Coulon, P.-Y.: Open or closed mouth state detection: static supervised classification based on log-polar signature. In: Advanced Concepts for Intelligent Vision Systems, vol. 5259, pp. 1093–1102. Springer, Heidelberg, Germany (2008)
Minotto, V.P., Lopes, C.B.O., Scharcanski, J., Jung, C.R., Lee, B.: Audio-visual voice activity detection based on microphone arrays and color information. IEEE J. Sel. Top. Sig. Process. 7(1), 147–156 (2013)
Vural, E., Cetin, M., Ercil, A., Littlewort, G., Bartlett, M., Movellan, J.: Drowsy driver detection through facial movement analysis. In: Proceedings of the ICCV Workshop on Human Computer Interaction, pp. 6–18 (2007)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Omidyeganeh, M., Shirmohammadi, S., Abtahi, S., Khurshid, A., Farhan, M., Scharcanski, J., Hariri, B., Laroche, D., Martel, L.: Yawning detection using embedded smart cameras. IEEE Trans. Instrum. Meas. 65(3), 570–582 (2016)
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv (2014). arXiv:1409.1556
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, pp. 807–814 (2010)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)
Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., Hariri, B.: YawDD: a yawning detection dataset. In: Proceedings of the ACM Multimedia Systems, pp. 24–28 (2014)
Chiou, C.-Y., Wang, W.-C., Lu, S.-C., Huang, C.-R., Chung, P.-C., Lai, Y.-Y.: Driver monitoring using sparse representation with part-based temporal face descriptors. IEEE Trans. Intell. Transp. Syst. (2019) (early access)
Yu, J., Park, S., Lee, S., Jeon, M.: Driver drowsiness detection using condition-adaptive representation learning framework. IEEE Trans. Intell. Transp. Syst. (2019) (early access)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625–2634 (2015)
Weng, C.H., Lai, Y.H., Lai, S.H.: Driver drowsiness detection via a hierarchical temporal deep belief network. In: Proceedings of the Asian Conference on Computer Vision, pp. 117– 33. Springer (2016)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. ArXiv (2017). [online] Available: https://arxiv.org/abs/1704.04861
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Sreelakshmi, K.K., Ranjani, J.J. (2021). A Non-invasive approach for Driver Drowsiness Detection using Convolutional Neural Networks. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_13
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DOI: https://doi.org/10.1007/978-981-15-5788-0_13
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