Driver Drowsiness Detection Based on Face Feature and Perclos
DOI:
https://doi.org//10.32628/IJSRST218319Keywords:
PERCLOS, Computer Vision, Web Camera, Road AccidentsAbstract
Driving vehicles are complex and require undivided attention to prevent road accidents. Fatigue and distraction are a major risk factor that causes traffic accidents, severe injuries, and a high risk of death. Some progress has been made for driver drowsiness detection using a contact-based method that utilizes vehicle parts (such as steering angle and pressure on the pedal) and physiological signals (electrocardiogram and electromyogram). However, a contactless system is more potential for real-world conditions. In this study, we propose a computer vision-based method to detect driver's drowsiness from a video taken by a camera. The method attempts to recognize the face and then detecting the eye in every frame. From the detected eye, iris regions for left and right eyes are used to calculate the PERCLOS measure (the percentage of total time that eye is closed). The proposed method was evaluated based on public YawDD video dataset. The results found that PERCLOS value when the driver is alert is lower than when the driver is drowsy.
References
- Hitendra Garg. Drowsiness Detection of a Driver using Conventional Computer Vision Application. IEEE Transactions on Deep Learning Systems, 2019.
- S.Gopi, “Beacon Update for Greedy Perimeter Stateless Routing Protocol in MANETs” in International Journal of Computer Science and Mobile Applications-IJCSMA
- E.Punarselvam,“ Different loading condition and angle measurement of human lumbar spine MRI image using ANSYS”, Springer Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, https://doi. org/10.1007/s12652-020-01939-7
- H. Dinh, E. Jovanov, and R. Adhami. Eye blink detection using intensity vertical projection,2012.
- T. Danisman, I. Bilasco, C. Djeraba, and N. Ihaddadene. Drowsy driver detection system using eye blink patterns. In Machine and Web Intelligence (ICMWI), Oct 2010.
- M. Chau and M. Betke. Real time eye tracking and blink detection with USB cameras. Technical Report 2005- 12, Boston University Computer Science, May 2005.
- L. M. Bergasa, J. Nuevo, M. A. Sotelo, and M. Vazquez. Real-time system for monitoring driver vigilance. In IEEE Intelligent Vehicles Symposium, 2004.
- Good fellowI.J., Piecewise linear multilayer perceptrons and dropout.stat1050,22, 2013.
- Glorot,X.,Bengio,Y., Understanding the difficulty of training deep feed forward neural networks.,in:Aistats,pp.249–256, 2010.
- He,K.,Zhang,X.,Ren,S.,Sun,J., Deep residual learning for image recognition,in:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770–778, 2016.
- Jung,K Object recognition on mobile devices ,in:Consumer Electronics-Berlin(ICCE-Berlin),2012 IEEE International Conference on, IEEE. pp.258–262, 2012.
- Kingma,D.,Ba,J. Adam:A method for stochastic Optimization . arXiv preprint arXiv:1412.6980, 2014.
- Krizhevsky,A.,Sutskever,I.,Hinton,G.E. Image net classification with deep convolutional neural networks, in:Advances in neural information processing systems, pp.1097–1105, 2012.
- Le,Q.V.,Karpenko,A.,Ngiam,J.,Ng,A.Y. Ica with reconstruction cost for efficient over complete feature learning, in:Advances in Neural Information Processing Systems, pp.1017–1025, 2011.
- LeCun,Y., Boser,B., Denker,J.S., Henderson,D., Howard,R.E., Hubbard,W.,Jackel, L.D. Back propagation applied to hand written zip code recognition. Neural computation 1,541–551, 1989.
- LeCun, Y.,Bottou, L.,Bengio, Y.,Haffner,P a.Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 2278–2324, 1998.
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