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2023 | OriginalPaper | Buchkapitel

Tracking of Driver Behaviour and Drowsiness in ADAS

verfasst von : Oleg Evstafev, Sergey Shavetov

Erschienen in: Cyber-Physical Systems and Control II

Verlag: Springer International Publishing

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Abstract

This paper focuses on the development of a non-intrusive driver warning system as part of ADAS (Advanced Driver Assistance Systems) to help improve the safety of all road users, when driving, on the road. The proposed algorithm uses computer vision, implemented based on facial landmark detection, to detect driver drowsiness based on the driver’s eye condition. This algorithm has shown good results with HOG + Linear SVM for searching and locating faces in the image, as well as determining the eye condition of the driver with and without glasses. If the eyes remain closed longer than expected or if the driver is not looking straight ahead, it is an indication that the driver is drowsy or tired, the system then sends a warning signal to the driver.

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Raviteja, S., Shanmughasundaram, R.: Advanced driver assistance system (ADAS). In: Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems, ICICCS 2018, pp. 737–740 (2019). DOI: https://doi.org/10.1109/ICCONS.2018.8663146

Althoff, M., Stursberg, O., Buss, M.: Model-based probabilistic collision detection in autonomous driving. IEEE Trans. Intell. Transp. Syst. 10, 299–310 (2009). https://doi.org/10.1109/TITS.2009.2018966CrossRef

Zhang, G., Yau, K.K.W., Zhang, X., Li, Y.: Traffic accidents involving fatigue driving and their extent of casualties. Accid. Anal. Prev. 87, 34–42 (2016). https://doi.org/10.1016/j.aap.2015.10.033CrossRef

Khan, M.Q., Lee, S.: A comprehensive survey of driving monitoring and assistance systems. Sensors 19(11), 2574 (2019)CrossRef

Yu, J., Chen, Z., Zhu, Y., Chen, Y.J., Kong, L., Li, M.: Finegrained abnormal driving behaviors detection and identification with smartphones. IEEE Trans. Mob. Comput. 16(8), 2198–2212 (2016)CrossRef

Budak, U., Bajaj, V., Akbulut, Y., Atila, O., Sengur, A.: An effective hybrid model for EEG-based drowsiness detection. IEEE Sens. J. 19(17), 7624–7631 (2019)CrossRef

Ngxande, M., Tapamo, J.R., 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, pp. 156–161 (2017)

Cech, J., Franc, V., Matas, J.: A 3D approach to facial landmarks: detection, refinement, and tracking. In: Proc. International Conference on Pattern Recognition (2014)

Lee, H., Lee, J., Shin, M.: Using wearable ECG/PPG sensors for driver drowsiness detection based on distinguishable pattern of recurrence plots. Electronics 8(2), 192 (2019)CrossRef

10.

Kim, J.Y., Jeong, C.H., Jung, M.J., Park, J.H., Jung, D.H.: Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving. Int. J. Automot. Technol. 14(6), 965–970 (2013). https://doi.org/10.1007/s12239-013-0106-zCrossRef

11.

Zheng, W.L., 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. IEEE Transactions on Intelligent Transportation Systems (2019)

12.

Vezzetti, E., Marcolin, F.: 3D human face description: Landmarks measures and geometrical features. Image Vis. Comput. 30, 698–712 (2012). https://doi.org/10.1016/j.imavis.2012.02.007CrossRef

13.

Wang, M.S., et al.: Drowsy behavior detection based ondriving information. Int. J. Automot. Technol. 17(1), 165–173 (2016)CrossRef

14.

Bačić, B., Zhang, J.: Towards Real-Time Drowsiness Detection for Elderly care.arXiv (2020)

15.

Zhao, L., Wang, Z., Wang, X., Liu, Q.: Driver drowsiness detection using facial dynamic fusion information and a DBN. In: IET Intelligent Transport Systems, vol. 12, no. 2, pp. 127–133 (2018)

16.

Massoz, Q., Langohr, T., Franois, C., Verly, J.G.: The ULg multimodality drowsiness database (called DROZY) and examples of use. In: 2016 IEEE WACV, pp. 1–7 (2016)

17.

Garca-Garca, M., Caplier, A., Rombaut, M.: Sleep deprivation detection for real-time driver monitoring using deep learning. In: International Conference Image Analysis and Recognition, pp. 435–442 (2018)

18.

Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., Hariri, B.: A yawning detection dataset. In: Proceedings of the 5th ACM Multimedia Systems Conference, pp. 24–28 (2014)

19.

Xing, Y., et al.: Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition. IEEE Trans. Comput. Soc. Syst. 5(1), 95–108 (2017)CrossRef

20.

Nguyen, T.P., Chew, M.T., Demidenko, S.: Eye tracking system to detect driver drowsiness. In: ICARA 2015—Proceedings of the 2015 6th International Conference on Automation, Robotics and Applications, pp. 472–477 (2015). DOI: https://doi.org/10.1109/ICARA.2015.7081194

21.

Hashemi, M., Mirrashid, A., Beheshti Shirazi, A.: Driver safety development: real-time driver drowsiness detection system based on convolutional neural network. SN Comput. Sci. 1(5), 1 (2020). https://doi.org/10.1007/s42979-020-00306-9CrossRef

22.

Jacobé de Naurois, C., Bourdin, C., Stratulat, A., Diaz, E., Vercher, J.L.: Detection and prediction of driver drowsiness using artificial neural network models. Accid. Anal. Prev. 126, 95–104 (2019). DOI: https://doi.org/10.1016/j.aap.2017.11.038

23.

Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detect. Driv. Drowsiness Based Sens.S: Re-View (2012). https://doi.org/10.3390/s121216937CrossRef

24.

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings—2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 886–893 (2005). DOI: https://doi.org/10.1109/CVPR.2005.177

25.

Viola, P.: The Viola/Jones face detector classifier is learned from labeled data. Procedia Comput. Sci., pp. 22–23 (2001)

26.

Robinson, J., Li, Y., Zhang, N., Fu, Y., Tulyakov, S.: Laplace landmark localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 10102–10111 (2019). DOI: https://doi.org/10.1109/ICCV.2019.01020

27.

Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1867–1874 (2014). DOI: https://doi.org/10.1109/CVPR.2014.241

28.

Boyko, N., Basystiuk, O., Shakhovska, N.: Performance evaluation and comparison of software for face recognition, based on Dlib and Opencv library. In: Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018, pp. 478–482 (2018). DOI: https://doi.org/10.1109/DSMP.2018.8478556

29.

Cech, J., Soukupova, T.: Real-Time Eye Blink Detection using Facial Landmarks. Cent. Mach. Perception. Dep. Cybern. Fac. Electr. Eng. Czech Tech. Univ, Prague (2016)

30.

Barkstrom, J., Barkstrom, J.: What is a Raspberry Pi? In: Introduction to the Raspberry Pi (2019)

Titel: Tracking of Driver Behaviour and Drowsiness in ADAS
verfasst von: Oleg Evstafev
Sergey Shavetov
Verlag: Springer International Publishing
Buch: Cyber-Physical Systems and Control II
Print ISBN: 978-3-031-20874-4

Electronic ISBN: 978-3-031-20875-1

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-20875-1_30

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