政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/130590

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/130590

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/130590

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/130590

Title:	以深度學習為基礎的睡意偵測技術 Drowsiness detection based on deep learning approach
Authors:	陳研佑 Chen, Yen-You
Contributors:	廖文宏 Liao, Wen-Hung 陳研佑 Chen, Yen-You
Keywords:	深度學習電腦視覺睡意偵測
Date:	2020
Issue Date:	2020-07-01 13:50:23 (UTC+8)
Abstract:	汽車駕駛在開車的途中發生打瞌睡行為的話很容易造成車禍發生，可能會造成行人或是駕駛受傷甚至是死亡。為了避免因為駕駛的打瞌睡行為而發生車禍，我們設計了一套可以自動偵測開車的駕駛是否有打瞌睡行為的即時辨識系統，這套系統使用了電腦視覺技術以及影像處理相關的演算法來分析駕駛的臉部表情和動作資訊來判斷出是否有明顯睡意產生。此系統的處理流程可以分成主要兩個部分，其中一個是人臉偵測，另外一個是睡意辨識，為了有效的辨識駕駛的情況而先偵測並擷取臉部圖像，藉此來去除掉不必要的環境背景因素，在睡意辨識的部分我們則是提出了合併多種不同的深度學習模型的方法來分析駕駛的睡意狀況。而在這次的研究中所使用到的訓練和測試的睡意資料集是由清華大學(NTHU)電腦視覺實驗室所提供的，其中我們所建立的睡意辨識模型在測試資料集上的辨識準確率可以達到87.34%，而本次的實驗結果也優於過去多數相關文獻的結果。
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
Description:	碩士國立政治大學資訊科學系 107753021
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0107753021
Data Type:	thesis
DOI:	10.6814/NCCU202000494
Appears in Collections:	[Department of Computer Science ] Theses

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