| Reference: | [1] Reza Ghoddoosian, Marnim Galib, and Vassilis Athitsos. A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection. arXiv:1904.07312, 2019.
[2] Tun-Huai Shih and Chiou-Ting Hsu. MSTN: Multistage spatial-temporal network for driver drowsiness detection. Springer, 146–153, 2016.
[3] Park S, Pan F, Kang S, and Yoo CD. Driver drowsiness detection system based on feature representation learning using various deep networks. Springer, 154–164, 2016.
[4] Krizhevsky A, Sutskever I, and Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems : 1097–1105, 2012.
[5] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, CVPR 2014
[6] Heming Yao, Wei Zhang, Rajesh Malhan, Jonathan Gryak, and Kayvan Najarian. Filter-Pruned 3D Convolutional Neural Network for Drowsiness Detection. IEEE, 1258–1262, 2018.
[7] Xuan-Phung Huynh, Sang-Min Park, and Yong-Guk Kim. Detection of driver drowsiness using 3d deep neural network and semi-supervised gradient boosting machine. Springer, pp. 134–145, ACCV 2016.
[8] Xuanhan Wang, Lianli Gao, Jingkuan Song, and Hengtao Shen. Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition. IEEE Signal Processing Letters, pp. 510–514, 2017.
[9] Liang Zhang, Peiyi Shen, and Juan Song. Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM. IEEE, 4517–4524, 2017.
[10] Koustav Mullick and Anoop M. Namboodiri. Learning Deep And Compact Models For Gesture Recognition. arXiv:1712.10136, 2017.
[11] Tianyi Liu, Shuangsang Fang, Yuehui Zhao, Peng Wang, and Jun Zhang. Implementation of Training Convolutional Neural Networks. arXiv:1506.01195, 2015.
[12] Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, and Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958, 2014
[13] https://en.wikipedia.org/wiki/Adaptive_histogram_equalization
[14] Zhang K, Zhang Z, Li Z, and Qiao Y. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499–1503, 2016
[15] Weng C-H, Lai Y-H, and Lai S-H. Driver drowsiness detection via a hierarchical temporal deep belief network. Springer: 117–133, 2016
[16] Krizhevsky, Alex, Sutskever, Ilya, and Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp. 1097–1105, 2012.
[17] Chollet François. Keras. 2015.
[18] Martín A, et al. Tensorflow: a system for large-scale machine learning. OSDI. Vol. 16. 2016.
[19] Gao Huang, Zhuang Liu, Kilian Q. Weinberger, and Laurens van der Maaten. Densely Connected Convolutional Networks. arXiv:1608.06993, 2016.
[20] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
[21] Jongmin Yu, Sangwoo Park, Sangwook Lee, and Moongu Jeon. Driver Drowsiness Detection Using Condition-Adaptive Representation Learning Framework. IEEE, 2018.
[22] Zaremba W and Sutskever I. Learning to execute. arXiv:1410.4615, 2014
[23] Jing-Ming Guo, Herleeyandi Markoni. Driver drowsiness detection using hybrid convolutional neural network and long short-term memory. Springer, 2018
[24] Jasper S.W, Jason T, Kerry A.N, Gideon D.P.A.A, Mark S. Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks. Springer, 2019
[25] Rateb Jabbar, Khalifa Al-Khalifa, Mohamed Kharbeche, Wael Alhajyaseen, Mohsen Jafari, Shan Jiang. Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques. IEEE ANT, 2018 |
