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題名 以深度學習為基礎的睡意偵測技術
Drowsiness detection based on deep learning approach作者 陳研佑
Chen, Yen-You貢獻者 廖文宏
Liao, Wen-Hung
陳研佑
Chen, Yen-You關鍵詞 深度學習
電腦視覺
睡意偵測日期 2020 上傳時間 1-Jul-2020 13:50:23 (UTC+8) 摘要 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。 參考文獻 [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 描述 碩士
國立政治大學
資訊科學系
107753021資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107753021 資料類型 thesis dc.contributor.advisor 廖文宏 zh_TW dc.contributor.advisor Liao, Wen-Hung en_US dc.contributor.author (Authors) 陳研佑 zh_TW dc.contributor.author (Authors) Chen, Yen-You en_US dc.creator (作者) 陳研佑 zh_TW dc.creator (作者) Chen, Yen-You en_US dc.date (日期) 2020 en_US dc.date.accessioned 1-Jul-2020 13:50:23 (UTC+8) - dc.date.available 1-Jul-2020 13:50:23 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2020 13:50:23 (UTC+8) - dc.identifier (Other Identifiers) G0107753021 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/130590 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 107753021 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.tableofcontents 致謝 II摘要 III目錄 IV表目錄 V圖目錄 VI第1章 緒論 1第1節 研究背景與動機 1第2節 睡意偵測 1第3節 研究目的 2第4節 論文架構 3第2章 技術背景與相關文獻研究 4第1節 卷積神經網路模型CNN 4第2節 臉部偵測模型MTCNN 5第3節 特徵分析模型LSTM 7第3章 資料的準備及處理 8第1節 睡意資料集 8第2節 資料預處理 9第3節 標籤預處理 10第4章 睡意偵測方法 12第1節 C3D模型架構 12第2節 CNN模型架構 13第3節 LSTM模型架構 15第4節 時間平滑後處理 16第5節 影像資料的讀取方式 18第6節 深度學習套件以及開發環境 19第5章 實驗與結果分析 20第1節 CNN模型的測試結果 20第2節 C3D模型的測試結果 21第3節 睡意辨識的測試結果 21第4節 睡意辨識的執行效能分析 23第6章 結論與未來相關研究 25參考文獻 27 zh_TW dc.format.extent 2738403 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107753021 en_US dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 電腦視覺 zh_TW dc.subject (關鍵詞) 睡意偵測 zh_TW dc.title (題名) 以深度學習為基礎的睡意偵測技術 zh_TW dc.title (題名) Drowsiness detection based on deep learning approach en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [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 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202000494 en_US
