Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network
Tóm tắt
Từ khóa
Tài liệu tham khảo
World Health Organization The top ten causes of death. [Online]. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
Yu J, Chen Z, Zhu Y, Chen YJ, Kong L, Li M. Fine-grained abnormal driving behaviors detection and identification with smartphones. IEEE Trans Mobile Comput. 2016;16(8):2198–212.
Budak U, Bajaj V, Akbulut Y, Atila O, Sengur A. An effective hybrid model for EEG-based drowsiness detection. IEEE Sens J. 2019;19(17):7624–31.
Ngxande M, Tapamo JR, Burke M. Driver drowsiness detection using behavioral measures and machine learning techniques :a review of state-of-art techniques. In: 2017 Pattern recognition association of South Africa and robotics and mechatronics. 2017;pp. 156-161.
Sahayadhas A, Sundaraj K, Murugappan M. Detecting driver drowsiness based on sensors: a review. Sensors. 2012;12(12):16937–53.
Kim JY, Jeong CH, Jung MJ, Park JH, Jung DH. Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving. Int J Automotive Technol. 2013;14(6):965–70.
Zheng WL, Gao K, Li G, Liu W, Liu C, Liu J, Q., & Lu, B. L. . Vigilance estimation using a wearable EOG device in real driving environment. In: IEEE Transactions on Intelligent Transportation Systems. 2019;.
Lee H, Lee J, Shin M. Using wearable ECG/PPG sensors for driver drowsiness detection based on distinguishable pattern of recurrence plots. Electronics. 2019;8(2):192.
Wang MS, Jeong NT, Kim KS, Choi SB, Yang SM, You SH, Suh MW. Drowsy behavior detection based on driving information. Int J Automotive Technol. 2016;17(1):165–73.
Deng W, Wu R. Real-time driver-drowsiness detection system using facial features. IEEE Access. 2019;7:118727–38.
Zhao L, Wang Z, Wang X, Liu Q. Driver drowsiness detection using facial dynamic fusion information and a DBN. IET Intell Trans Syst. 2018;12(2):127–33.
Verma B, Choudhary A. Deep learning based real-time driver emotion monitoring. In: 2018 IEEE international conference on vehicular electronics and safety. 2018;pp 1-6.
Mandal B, Li L, Wang GS, Lin J. Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans Intell Trans Syst. 2016;18(3):545–57.
Massoz Q, Langohr T, François C, Verly JG. The ULg multi modality drowsiness database (called DROZY) and examples of use. In: 2016 IEEE WACV. 2016;pp. 1-7
García-García M, Caplier A, Rombaut M. Sleep deprivation detection for real-time driver monitoring using deep learning. In: International conference image analysis and recognition. 2018;pp. 435-442.
Abtahi S, Omidyeganeh M, Shirmohammadi S, Hariri B. YawDD: A yawning detection dataset. In: Proceedings of the 5th ACM multimedia systems conference. 2014;pp. 24-28.
Xing Y, Lv C, Zhang Z, Wang H, Na X, Cao D, Wang FY. Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition. IEEE Trans Comput Soc Syst. 2017;5(1):95–108.
Kim KW, Lee WO, Kim YG, Hong HG, Lee EC, Park KR. Segmentation method of eye region based on fuzzy logic system for classifying open and closed eyes. Opt Eng. 2015;54(3):033103.
Ghoddoosian R, Galib M, Athitsos V. A Realistic dataset and baseline temporal model for early drowsiness detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2019;pp. 0-0.
Song F, Tan X, Liu X, Chen S. Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recognit. 2014;47(9):2825–38.
Zhao L, Wang Z, Zhang G, et al. Multimed Tools Appl. 2018;77:19415. https://doi.org/10.1007/s11042-017-5380-8.
Sukrit Mehta, et al. “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. 2019.
Bamidele A, et al. Non-intrusive driver drowsiness detection based on face and eye tracking. Int J. Adv. Comput. Sci. Appl. 2019;10:
Anitha J, Mani G, Venkata Rao K. Driver drowsiness detection using Viola Jones algorithm. In: Satapathy S, Bhateja V, Mohanty J, Udgata S, editors. Smart intelligent computing and applications.ion Smart innovation, systems and technologies, vol. 159. Singapore: Springer; 2020.
Ji Y et al. Fatigue state detection based on multi-index fusion and state recognition network. In: IEEE Access 7, 2019.
Liu W et al. ”Convolutional two-stream network using multifacial feature fusion for driver fatigue detection. Future Internet 11.5, 2019.
Ed-doughmi Y, Najlae I Driver Fatigue Detection using Recurrent Neural Networks. In: Proceedings of the 2nd international conference on networking, information systems & security. 2019.
Wanghua D, Wu R Real-time driver-drowsiness detection system using facial features. IEEE Access. 2019;7:
Jabbar R et al. Real-time driver drowsiness detection for android application using deep neural networks techniques. Proc Comput Sci. 130, 2018.
Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. 2001; 1, pp I-I.
Kazemi V, Sullivan J. One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2014;pp. 1867-1874.
Intelligent Behaviour Understanding Group (iBUG). https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/
Pan G, Sun L, Wu Z, Lao S. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: 2007 IEEE 11th international conference on computer vision. 2007;pp. 1-8.
Parmar SH, Jajal M, Brijbhan YP. Drowsy Driver Warning System Using Image Processing. 2014;1.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014.