Open Access
Issue |
ITM Web Conf.
Volume 54, 2023
2nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
|
|
---|---|---|
Article Number | 01011 | |
Number of page(s) | 8 | |
Section | Computing | |
DOI | https://doi.org/10.1051/itmconf/20235401011 | |
Published online | 04 July 2023 |
- K. Kumari, “Review on Drowsy Driving: Becoming Dangerous Problem,” International Journal of Science and Research, (2014). [Google Scholar]
- S. Mehta, S. Dadhich, S. Gumber, and A. J. Bhatt, “Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio Fatigue Detection Non-Intrusive Methods Driver monitoring system,” (2019). [Google Scholar]
- W. Deng and R. Wu, “Real-Time Driver-Drowsiness Detection System Using Facial Features,” IEEE Access, vol. 7, pp. 118727–118738, (2019). [CrossRef] [Google Scholar]
- T. Vesselenyi, S. Moca, A. Rus, T. Mitran, and B. Tataru, “Driver drowsiness detection using ANN image processing,” in IOP Conference Series: Materials Science and Engineering, vol. 252, no. 1, (2017). [Google Scholar]
- J. Gwak, A. Hirao, and M. Shino, “An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing,” Applied Sciences, vol. 10, no. 8, (2020). [Google Scholar]
- S.L.R. and S. Anchan, “Human Drowsiness Detection System,” International Journal of Engineering and Advanced Technology, vol. 8, no. pp. 316–319, Apr. (2019). [CrossRef] [Google Scholar]
- J. J. Yan, H. H. Kuo, Y. F. Lin, and T. L. Liao, “Real-time driver drowsiness detection system based on PERCLOS and grayscale image processing,” IEEE International Symposium on Computer, Consumer and Control, IS3C, pp. 243–246, (2016). [Google Scholar]
- T. Danisman, I. M. Bilasco, C. Djeraba, and N. Ihaddadene, “Drowsy driver detection system using eye blink patterns,” in 2010 International Conference on Machine and Web Intelligence, ICMWI 2010 - Proceedings, pp. 230–233, (2010). [CrossRef] [Google Scholar]
- P. Ebrahim, W. Stolzmann, and B. Yang, “Eye movement detection for assessing driver drowsiness by electrooculography,” in Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, pp. 4142–4148, (2013). [CrossRef] [Google Scholar]
- M. Gjoreski, M. Z. Gams, M. Lustrek, P. Genc, J. U. Garbas, and T. Hassan, “Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions from Physiological and Visual Signals,” IEEE Access, vol. 8, pp. 70590–70603, (2020). [CrossRef] [Google Scholar]
- C. Schwarz, J. Gaspar, T. Miller, and R. Yousefian, “The detection of drowsiness using a driver monitoring system,” Traffic Inj Prev, vol. 20, no. sup1, pp. S157–S161, (2019). [CrossRef] [Google Scholar]
- S. Himani Parmar, M. Jajal, and Y. Priyanka Brijbhan Electronics, “Drowsy Driver Warning System Using Image Processing | Drowsy Driver Warning System Using Image Processing,” IJEDR, (2019). [Google Scholar]
- C. B. S. Maior, M.J. Das C. Moura, J.M.M. Santana, and I.D. Lins, “Real-time classification for autonomous drowsiness detection using eye aspect ratio,” Expert Syst Appl, vol. 158, (2020). [Google Scholar]
- P. Wang and L. Shen, “A method of detecting driver drowsiness state based on multi-features of face,” in 2012 5th International Congress on Image and Signal Processing, CISP 2012, pp. 1171–1175, (2012). [CrossRef] [Google Scholar]
- M. Sabet, R. A. Zoroofi, K. Sadeghniiat-Haghighi, and M. Sabbaghian, “A new system for driver drowsiness and distraction detection,” in ICEE 2012 - 20th Iranian Conference on Electrical Engineering, pp. 1247–1251, (2012). [CrossRef] [Google Scholar]
- M. Dua, Shakshi, R. Singla, S. Raj, and A. Jangra, “Deep CNN models-based ensemble approach to driver drowsiness detection,” Neural Comput Appl, vol. 33, no. 8, pp. 3155–3168, (2021). [CrossRef] [Google Scholar]
- S. Liu, Y. Li, C. Wen, and S. Wang, “A Novel Driver Drowsiness Detection Method Based on Heart Rate and Its Variability,” IEEE Access, vol. 9, pp. 114103–114114, (2021). [Google Scholar]
- F. Li, X. Zhao, M. Liu, and H. Han, “Driver drowsiness detectionbased on multi-task learning and EEG signals,” Computer Methods and Programs in Biomedicine, vol. 205, (2022). [Google Scholar]
- S. Anwar, K. Khan, A. Rehman, and M. Baig, “A novel driver drowsiness detection system using non-contact vital signs and machine learning techniques,” Biomedical Signal Processing and Control, vol. 68, (2021). [Google Scholar]
- C. Liu, J. Lu, Y. Zhang, and Y. Yang, “Driver drowsiness detection using multi-task learning and a convolutional neural network,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 1748–1758, (2022). [Google Scholar]
- H. He, Y. Zhou, X. Zhang, and J. Zhang, “Driver drowsiness detection using multi-view convolutional neural networks,” IEEE Transactions onIntelligent Transportation Systems, vol. 23, no. 3, pp. 1248–1258, (2022). [Google Scholar]
- H. Li, Z. Li, Y. Li, and Y. Li, “A novel driver drowsiness detection method based on deep learning and physiological signals,” Measurement, vol. 190, (2022). [Google Scholar]
- N. K. Ibraheem and S. S. Yahya, “Real-time driver drowsiness detection system using machine learning algorithms,” SN Computer Science, vol. 2, no. 3, (2021). [Google Scholar]
- A. Khan, A. Qayyum, and H. Bokhari, “Driver drowsiness detection system using deep learning and handcrafted features,” IEEE Access, vol. 9, pp. 64416–64427, (2021). [Google Scholar]
- A. Sharma and R. Pandey, “Efficient driver drowsiness detection using transfer learning with deep neural networks,” Computers Electrical Engineering, vol. 91, (2021). [Google Scholar]
- S. Reddy, S. K. Arora, and S. K. Rautaray, “Driver Drowsiness Detectionusing Deep Learning and Effective Fusion of Multi-modal Data,” PatternRecognition Letters, vol. 154, pp. 11–20, (2022). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.