Drivers' Real-Time Drowsiness Identification Using Facial Features and Automatic Vehicle Speed Control

Natraj N.A; Bhavani S

doi:10.34256/ijcci2114

Natraj N.A Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
Bhavani S Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.

Keywords: Eye aspect ratio, Facial landmarks, Drowsiness detection

Abstract

The road crash is one of the significant problems that is of great concern in today's world. Road accidents are often caused by drivers' carelessness and negligence. The drowsy condition of the drivers, which occurs due to overwork, fatigue, and many other factors, is one of those causes. It is therefore most critical to establish systems that can detect the driver's drowsy state and provide the drivers with the appropriate warning system. In addition to the automatic speed control of the car, this system thus supports drivers in incidents by providing warnings in advance. This means that road collisions that are harmful to living lives are minimised. This is achieved by using the technique of image recognition, where driver drowsiness is observed, and using this method, simultaneous warning and speed monitoring of the vehicle is carried out.

Metrics

Metrics Loading ...

References

Asthana, A. Zafeoriou, S., Cheng, S., Pantic M., (2014). Incremental face alignment in the wild. In Conference on Computer Vision and Pattern Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, 1859-1866. https://doi.org/10.1109/CVPR.2014.240

Bergasa, L.M., Nuevo, J., Sotelo, M.A., Vazquez, M., (2004). Real-time system for monitoring driver vigilance. In IEEE Intelligent Vehicles Symposium, 78-83.

Cech, J., Franc, V., Matas, J., (2014). A 3D approach to facial landmarks: Detection, reﬁnement, and tracking. 22nd International Conference on Pattern Recognition, IEEE, https://doi.org/10.1109/ICPR.2014.378

Chau, M., Betke, M., (2005). Real time eye tracking and blink detection with USB cameras. Boston University Computer Science Technical Report, 1-10.

Danisman, T., Belasco, I., Djeraba, C., Ihaddadene N., (2010). Drowsy driver detection system using eye blink patterns. In 2010 International Conference on Machine and Web Intelligence, IEEE, 230-233. https://doi.org/10.1109/ICMWI.2010.5648121

Dinh, H., Jovanov, E., Adhami, R., (2012). Eye blink detection using intensity vertical projection. In International Multi-Conference on Engineering and Technological Innovation, IMETI.

Divjak, M., Bischof, E., (2009). Eye blink-based fatigue detection for prevention of computer vision syndrome. In IAPR Conference on Machine Vision Applications, 350- 353.

Drutarovsky, T., Fogelton, A., (2014). Eye blink detection using variance of motion vectors. In European Conference on Computer Vision - ECCV 2014 Workshops, 436-448. https://doi.org/10.1007/978-3-319-16199-0_31

Lee, W.H., Lee, E.C., Park, K.E., (2010). Blink detection robust to various facial poses. Journal of Neuroscience Methods, 193(2) 356-372.

Pan, G., Sun, L., Wu, Z., Lao, S., (2007). Eye blink-based anti-spooﬁng in face recognition from a generic web camera. In 2007 IEEE 11th international conference on computer vision, IEEE, 1-8. https://doi.org/10.1109/ICCV.2007.4409068

Ren, S., Cao, X., Wei, Y., Sun, J., (2014). Face alignment at 3000 fps via regressing local binary features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 1685-1692. https://doi.org/10.1109/CVPR.2014.218

Sahayadha, A., Sundaraj, K., Murugappan, M., (2012). Detecting driver drowsiness based on sensors: A review. sensors, 21 (2) 16937- 16953. https://doi.org/10.3390/s121216937