A Low-Cost Vehicle Assistance System for Detection of Critical Driving Situations

Hlavatý, Michal; Cigánek, Ján; Kozáková, Alena; Kučera, Erik; Haffner, Oto

doi:10.2478/scjme-2024-0033

Abstract

The paper presents a low-cost driver fatigue detection camera system for older car models that do not have built-in fatigue detection systems. Based on a review of current solutions and image processing methods for face and eye detection, the Viola-Jones method for face detection and the cascade regression for eye detection were selected to enable real-time detection via smartphones and avoid the high cost of traditional systems. The proposed system has been successfully tested with real-time video under laboratory conditions.

References

Zhang, H., Ni, D., Ding, N., Sun, Y., Zhang, Q., Li, X. “Structural analysis of driver fatigue behavior: A systematic review”, Transportation Research Interdisciplinary Perspectives 21, 2023. DOI: 10.1016/j.trip.2023.100865
Search in Google Scholar Back to article
El-Nabi, S. A., El-Shafai, W., El-Rabaie, E. S. M., Ramadan, K. F., Abd El-Samie, F. E., Mohsen, S. “Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review”, Multimed. Tools Appl., 2023. DOI: 10.1007/s11042-023-15054-0
Search in Google Scholar Back to article
Ueno, H., Kaneda, M., Tsukino, M. “Development of drowsiness detection system”, 2002. DOI: 10.1109/vnis.1994.396873
Search in Google Scholar Back to article
Saini, V. “Driver Drowsiness Detection System and Techniques : A Review”, Int. J. Comput. Sci. Inf. Technol. 5 (3), 2014.
Search in Google Scholar Back to article
Chai, M., Li, S. W., Sun, W. C., Guo, M. Z., Huang M. Y. “Drowsiness monitoring based on steering wheel status”, Transp. Res. Part D Transp. Environ. 66, 2019. DOI: 10.1016/j.trd.2018.07.007
Search in Google Scholar Back to article
Rongben, W., Lie, G., Bingliang, T., Lisheng, J. “Monitoring mouth movement for driver fatigue or distraction with one camera”, 2004. DOI: 10.1109/itsc.2004.1398917
Search in Google Scholar Back to article
Ji, Q., Zhu, Z., Lan, P. “Real-time nonintrusive monitoring and prediction of driver fatigue”, IEEE Trans. Veh. Technol. 53 (4), 2004. DOI: 10.1109/TVT.2004.830974
Search in Google Scholar Back to article
Čolić, A., Marques, O., Furht, B., “Driver Drowsiness detection System and Solutions”, 2014.
Search in Google Scholar Back to article
Of, O., Carriers, M. “PERCLOS : A Valid Psychophysiological Measure of Alertness as Assessed by Psychomotor Vigilance”, October 31 (5), 1998.
Search in Google Scholar Back to article
Škoda auto s.r.o. “Fatigue recognition assistant”, [Online] Available at: https://eshop.skoda-auto.sk/en_SK/fatigue-recognition-assistant/p/5E0054801
Search in Google Scholar Back to article
Volkswagen Group, “The Transporter 6.1 Optional Extra Brochure”, 2020. [Online]. Available at: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiq6sCk6IKEAxWq0AIHHWNyDBUQFnoECDQQAQ&url=https%3A%2F%2Fwww.volkswagenvans.ie%2Fidhub%2Fcontent%2Fdam%2Fonehub_nfz%2Fimporters%2Fie%2Fmodels%2Fdownloads%2Foptional-ext
Search in Google Scholar Back to article
I. Rear View Safety, “iVue Driver Fatigue System”. [Online]. Available at: https://www.rearviewsafety.com/driver-fatigue-system-rvs-335.html
Search in Google Scholar Back to article
Kumar, A., Kaur, A., Kumar, M. “Face detection techniques: a review”, Artif. Intell. Rev. 52 (2), 2019. DOI: 10.1007/s10462-018-9650-2
Search in Google Scholar Back to article
Hatem, H., Beiji, Z., Majeed, R. “A Survey of Feature Base Methods for Human Face Detection”, Int. J. Control Autom. 8 (5), 2015. DOI: 10.14257/ijca.2015.8.5.07
Search in Google Scholar Back to article
Anila, S., Devarajan, N. “Simple and Fast Face Detection System Based on Edges”, Int. J. Univers. Comput. Sci. 1 (2), 2010.
Search in Google Scholar Back to article
Yao, J., Cham, W. K. “Efficient model-based linear head motion recovery from movies”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2, 2004. DOI: 10.1109/cvpr.2004.1315193
Search in Google Scholar Back to article
Jones, M., Viola, P. “Fast multi-view face detection”, Mitsubishi Electr. Res. Lab TR-20003-96 3.14, 2003.
Search in Google Scholar Back to article
Viola P., Jones, M. “Rapid object detection using a boosted cascade of simple features”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1, 2001. DOI: 10.1109/cvpr.2001.990517
Search in Google Scholar Back to article
Ahmed, M. I. B., et al., “A Deep-Learning Approach to Driver Drowsiness Detection”, Safety 9 (3), 2023. DOI: 10.3390/safety9030065
Search in Google Scholar Back to article
Guede-Fernández, F., Fernández-Chimeno, M., Ramos-Castro, J., García-González, M. A. “Driver Drowsiness Detection Based on Respiratory Signal Analysis”, IEEE Access 7, 2019. DOI: 10.1109/ACCESS.2019.2924481
Search in Google Scholar Back to article
Wu, Y., Ji, Q. “Facial Landmark Detection: A Literature Survey”, Int. J. Comput. Vis. 127 (2), 2019. DOI: 10.1007/s11263-018-1097-z
Search in Google Scholar Back to article
Ferkova, Z., Matula, P. “Multimodal Point Distribution Model for Anthropological Landmark Detection”, in Proceedings - International Conference on Image Processing, ICIP 2019, September, 2019. DOI: 10.1109/ICIP.2019.8803252
Search in Google Scholar Back to article
Wang, N., Gao, X., Tao, D., Yang, H., Li, X. “Facial feature point detection: A comprehensive survey”, Neurocomputing 275, 2018. DOI: 10.1016/j.neucom.2017.05.013
Search in Google Scholar Back to article
Kazemi, V., Sullivan, J. “One millisecond face alignment with an ensemble of regression trees”, 2014. DOI: 10.1109/CVPR.2014.241
Search in Google Scholar Back to article
Milesich, T., Danko, J. Bucha, J. “Neural Networks - A Way to Increase the Fuel Efficiency of Vehicles”, Strojnícky časopis – Journal of Mechanical Engineering 68 (1), pp. 81 – 88, 2018. DOI: 10.2478/scjme-2018-0008
Search in Google Scholar Back to article
Mishra, A. “Machine Learning Algorithm for Surface Quality Analysis of Friction Stir Welded Joint”, Strojnícky časopis – Journal of Mechanical Engineering 70 (2), pp. 11 – 20, 2020. DOI: 10.2478/scjme-2020-0016
Search in Google Scholar Back to article
Ying, X. “An Overview of Overfitting and its Solutions”, in Journal of Physics: Conference Series 1168 (2), 2019. DOI: 10.1088/1742-6596/1168/2/022022
Search in Google Scholar Back to article
Luo, P., Wang, X., Tang, X. “Hierarchical face parsing via deep learning,” 2012. DOI: 10.1109/CVPR.2012.6247963
Search in Google Scholar Back to article
Sun, Y., Wang, X., Tang, X. “Deep convolutional network cascade for facial point detection”, 2013. DOI: 10.1109/CVPR.2013.446
Search in Google Scholar Back to article
Chen, Y., Yang, J., Qian, J. “Recurrent neural network for facial landmark detection”, Neurocomputing 219, 2017. DOI: 10.1016/j.neucom.2016.09.015
Search in Google Scholar Back to article

A Low-Cost Vehicle Assistance System for Detection of Critical Driving Situations

Abstract

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