Volume 7 • Issue 2 • PP: 91-99 • 2022
Driver Drowsiness Detection in Real-time
View PDF Abstract
In modern life, drowsiness is one of the major causes of road accidents, many of which are fatal. Analyzing statistics, it can be assumed that most road accidents occur as a result of drowsiness leading to serious injury and death. For this reason, various studies have been done on designing programs that can detect driver fatigue and alert them before a serious error occurs. This prevents them from falling asleep and having an accident. Some of the most common methods use automotive-based methods to design their own system. But these traditional measures were strongly influenced by other factors such as road structure, vehicle type and driver-wheel driveability. Some methods use psychological methods of their system that often provide the most accurate and consistent results in the driver's drowsiness monitoring. However, such techniques are very tedious as the electrodes need to be placed on the head and body. In addition, few studies are available where independent measurements are used as system installation, but such methods can confuse the driver and lead to unintended consequences. In this paper, we have proposed a non-disruptive and real-time program. Our proposed system classifies it as sleep deprivation. The model is fed with a large database of closed eyes and open eyes to produce results. The driver is notified by Buzz every time he is found drowsy. In our model, we use a standard forward-looking smartphone camera and use the information we have gained to produce results on our website. This can be more economical than using additional hardware.
Keywords
References
1. K. Dwivedi, K. Biswaranjan, and A. Sethi, ―Drowsy driver detection using representation learning‖, in 2014 IEEE International Advance Computing Conference (IACC), 2014, pp. 995–999.
2. S. Sangle, B. Rathore, R. Rathod, A. Yadav, and A. Yadav, ―Real Time Drowsiness Detection System‖, IOSR Journal of Computer Engineering (IOSR-JCE), pp. 87–92, 2018.
3. M. Shakeel, N. Bajwa, A. Anwaar, A. Sohail, A. Khan, and H. U. Rashid, Detecting Driver Drowsiness in Real Time Through Deep Learning Based Object Detection, 05 2019.
4. S. Mehta, S. Dadhich, S. Gumber, and A. Bhatt, ―Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio,‖ SSRN Electronic Journal, 01 2019.
5. J. D. Fuletra and D. Bosamiya, ―A Survey on Driver‘s Drowsiness Detection Techniques‖, International Journal on Recent and Innovation Trends in Computing and Communication, pp. 816–819, 11 2013.
6. R. Behera and K. Das, ―A Survey on Machine Learning: Concept, Algorithms and Applications,‖ International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, 02 2017.
7. S. Podder and S. Roy, ―Driver‘s drowsiness detection using eye status to improve the road safety‖, International Journal of Innovation Research in Computer and Communication Engineering, vol. 1, no. 7, 2013.
8. S. Junawane, S. Jagtap, P. Deshpande, and L. Soni, ―Driver Drowsiness Detection Techniques: A Survey‖, vol. 6, no. 11, pp. 2015–2017.
9. T. Soukupova and J. Cech, ―Real-time eye blink detection using facial landmarks‖, Computer Vision Winter Workshop, 2016.
10. F. Omidi and G. Nasleseraji, ―Non-intrusive Methods used to Determine the Driver Drowsiness: Narrative Review Articles‖, International Journal of Occupational Hygiene, vol. 8, no. 3, pp. 186–191, 2016.
11. S. S. Nagargoje and D. S. Shilvant, ―Drowsiness Detection System for Car Assisted Driver Using Image Processing‖, International Journal of Electrical and Electronics Research ISSN, vol. 3, no. 4, pp. 175–179, 2015.
12. Tejasweenimusale, Prof B, H. Pansambal, ‖Real Time Driver Drowsiness Detection System using Image Processing‖, IJREAM, vol. 2, no. 8, 2016.
13. R. Salakhutdinov and G. E. Hinton, ―An efficient learning procedure for deep Boltzmann machines‖, Neural Computation August 2012, vol. 24, no. 8: 1967–2006.
14. G. E. Hinton, S. Osindero, and Y. Teh. ―A fast learning algorithm for deep belief nets‖, Neural Computation
18:1527-1554, 2006.
15. Y. LeCun, Y. Bengio. ―Convolutional networks for images, speech, and time series‖, Handbook of Brain Theory and Neural Networks, MIT Press. pp. 3361, 1995. 16. H. Chen, A. F. Murray, ―Continuous restricted Boltzmann machine with an implementable training algorithm‖, Vision, Image and Signal Processing, IEEE Proceedings, vol. 150, no. 3, pp. 153-158, IET, 06 2003.
17. R. Salakhutdinov, A. Mnih, G. Hinton, ―Restricted Boltzmann machines for collaborative filtering‖, In Proc. of the 24th international conference on Machine learning, pp. 791-798, ACM, 06 2007.
18. P. J. Angeline, G. M. Saunders and J. B. Pollack, "An evolutionary algorithm that constructs recurrent neural networks," in IEEE Transactions on Neural Networks, vol. 5, no. 1, pp. 54-65, 01 1994.
19. D. P. Mandic, J. Chambers, ―Recurrent Neural Networks for prediction: learning algorithms, architectures and stability‖, John Wiley & Sons, Inc, 09 2001.
20. G. E. Hinton, R. S. Zemel, ―Autoencoders, Minimum Description Length, and Helmholtz Free Energy‖, Advances in Neural Information Processing Systems, pp. 3-3, 1994.
21. D. Patel, ―Explanation of Convolutional Neural Network - Deep Learning Tutorial (Tensorflow & Python),‖ Accessed on: Sep. 27, 2021. [Online]. Available: https://youtu.be/zfiSAzpy9NM
22. S. Tsang, ―MobileNetV1 — Depthwise Separable Convolution (Light Weight Model),‖ Accessed on: Sep. 30, 2021. [Online]. Available: https://towardsdatascience.com/a382df364b69
Cite This Article
Choose your preferred format