A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit
Email:
vtlinh@utc2.edu.vn
Từ khóa:
Drowsiness detection, image sensing, HOG, Raspberry Pi 3, safety driver for Vietnamese.
Tóm tắt
In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”Tài liệu tham khảo
[1] WHO. Road Safety. The global status report on road safety 2018. https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/
[2] Vietnam National Traffic Safety Committee, 2019. http://tuyengiao.vn/uy-ban-an-toan-giao-thong/trong-quy-i2019-xay-ra-tren-4-000-vu-tai-nan-giao-thong-120125. (In Vietnamese)
[3] A. Sahayadhas, K. Sundaraj, M. Murugappan, Detecting driver drowsiness based on sensors: A review, Sensors, 12 ( 2012) 16937-16953. https://doi.org/10.3390/s121216937
[4] M.A, Assari, M. Rahmati, Driver drowsiness detection using face expression recognition, in Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Kuala Lumpur, Malaysia, 337–341, 2011. DOI: 10.1109/ICSIPA.2011.6144162
[5] S. Ahn, T. Nguyen, H. Jang, J. G. Kim, S.C. Jun, Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data, Front. Hum. Neurosci., 10 (2016) 219. https://doi.org/10.3389/fnhum.2016.00219
[6] N. Agrawal, S. Singhal, Smart drip irrigation system using raspberry Pi and Arduino, in Proceedings of International Conference on Computing, Communication and Automation, Noida, India,928-932, 2015. DOI: 10.1109/CCAA.2015.7148526
[7] J. Marot, S. Bourennane, Raspberry Pi for image processing education, in Proceedings of 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, 2017. DOI: 10.23919/EUSIPCO.2017.8081633
[8] P. Viola, M. Jones, 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, Kauai, HI, USA, 2001. DOI: 10.1109/CVPR.2001.990517
[9] V. Kazemi, J. Sullivan, One Millisecond Face Alignment with an Ensemble of Regression Trees paper, in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, USA,1867-1874, 2014, DOI: 10.1109/CVPR.2014.241
[10] N. Dalal, B. Triggs, Histogram of Oriented Gradients for Human Detection, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 2005. DOI: 10.1109/CVPR.2005.177
[11] C. Meng, L. Shi-wu, S. Wen-cai, G. Meng-zhu, H. Meng-yuan, Drowsiness monitoring based on steering wheel status, Transportation Research Part D: Transport and Environment, 66 (2019) 95-103. https://doi.org/10.1016/j.trd.2018.07.007
[12] S.Arefnezhad, S. Samiee, A. Eichberger, A. Nahvi, Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection, Sensors 19 (2019) 943.
[13] G. Li, C. Wan-Young, Combined EEG-Gyroscope-tDCS Brain Machine Interface System for Early Management of Driver Drowsiness, IEEE Transactions on Human-Machine Systems, 48 (2018) 50-62. DOI: 10.1109/THMS.2017.2759808
[14] B.-G. Lee, B.-L. Lee, W.-Y. Chung, Wristband-type driver vigilance monitoring system using smartwatch, IEEE Sensors Journal, 15 (2015) 5624–5633.
[15] M. R. Guedira, A. El Qadi, M. R. Lrit, M. E. Hassouni, A novel method for image categorization based on histogram oriented gradient and support vector machine, in Proceedings of the International Conference on Electrical and Information Technologies, Rabat, Morocco, 2017. DOI: 10.1109/EITech.2017.8255229
[16] T. Soukupova, J. Cechin, Real-Time Eye Blink Detection using Facial Landmarks, in Proceedings of the 21st Computer Vision Winter Workshop, Rimske Toplice, Slovenia, 2016.
[2] Vietnam National Traffic Safety Committee, 2019. http://tuyengiao.vn/uy-ban-an-toan-giao-thong/trong-quy-i2019-xay-ra-tren-4-000-vu-tai-nan-giao-thong-120125. (In Vietnamese)
[3] A. Sahayadhas, K. Sundaraj, M. Murugappan, Detecting driver drowsiness based on sensors: A review, Sensors, 12 ( 2012) 16937-16953. https://doi.org/10.3390/s121216937
[4] M.A, Assari, M. Rahmati, Driver drowsiness detection using face expression recognition, in Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Kuala Lumpur, Malaysia, 337–341, 2011. DOI: 10.1109/ICSIPA.2011.6144162
[5] S. Ahn, T. Nguyen, H. Jang, J. G. Kim, S.C. Jun, Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data, Front. Hum. Neurosci., 10 (2016) 219. https://doi.org/10.3389/fnhum.2016.00219
[6] N. Agrawal, S. Singhal, Smart drip irrigation system using raspberry Pi and Arduino, in Proceedings of International Conference on Computing, Communication and Automation, Noida, India,928-932, 2015. DOI: 10.1109/CCAA.2015.7148526
[7] J. Marot, S. Bourennane, Raspberry Pi for image processing education, in Proceedings of 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, 2017. DOI: 10.23919/EUSIPCO.2017.8081633
[8] P. Viola, M. Jones, 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, Kauai, HI, USA, 2001. DOI: 10.1109/CVPR.2001.990517
[9] V. Kazemi, J. Sullivan, One Millisecond Face Alignment with an Ensemble of Regression Trees paper, in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, USA,1867-1874, 2014, DOI: 10.1109/CVPR.2014.241
[10] N. Dalal, B. Triggs, Histogram of Oriented Gradients for Human Detection, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 2005. DOI: 10.1109/CVPR.2005.177
[11] C. Meng, L. Shi-wu, S. Wen-cai, G. Meng-zhu, H. Meng-yuan, Drowsiness monitoring based on steering wheel status, Transportation Research Part D: Transport and Environment, 66 (2019) 95-103. https://doi.org/10.1016/j.trd.2018.07.007
[12] S.Arefnezhad, S. Samiee, A. Eichberger, A. Nahvi, Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection, Sensors 19 (2019) 943.
[13] G. Li, C. Wan-Young, Combined EEG-Gyroscope-tDCS Brain Machine Interface System for Early Management of Driver Drowsiness, IEEE Transactions on Human-Machine Systems, 48 (2018) 50-62. DOI: 10.1109/THMS.2017.2759808
[14] B.-G. Lee, B.-L. Lee, W.-Y. Chung, Wristband-type driver vigilance monitoring system using smartwatch, IEEE Sensors Journal, 15 (2015) 5624–5633.
[15] M. R. Guedira, A. El Qadi, M. R. Lrit, M. E. Hassouni, A novel method for image categorization based on histogram oriented gradient and support vector machine, in Proceedings of the International Conference on Electrical and Information Technologies, Rabat, Morocco, 2017. DOI: 10.1109/EITech.2017.8255229
[16] T. Soukupova, J. Cechin, Real-Time Eye Blink Detection using Facial Landmarks, in Proceedings of the 21st Computer Vision Winter Workshop, Rimske Toplice, Slovenia, 2016.
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
29/05/2019
Nhận bài sửa
24/06/2019
Chấp nhận đăng
23/08/2019
Xuất bản
15/11/2019
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Dao Thanh, T., & Thien Linh, V. (1573750800). A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit. Tạp Chí Khoa Học Giao Thông Vận Tải, 70(3), 184-192. https://doi.org/10.25073/tcsj.70.3.26
Số lần xem tóm tắt
183
Số lần xem bài báo
217
