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Reseach Article

Real-Time Safety Automobile Driver System

by Azmi Shawkat Abdulbaki, Samir Abdulrasoul Khadim
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 17
Year of Publication: 2015
Authors: Azmi Shawkat Abdulbaki, Samir Abdulrasoul Khadim
10.5120/ijca2015906803

Azmi Shawkat Abdulbaki, Samir Abdulrasoul Khadim . Real-Time Safety Automobile Driver System. International Journal of Computer Applications. 130, 17 ( November 2015), 34-38. DOI=10.5120/ijca2015906803

@article{ 10.5120/ijca2015906803,
author = { Azmi Shawkat Abdulbaki, Samir Abdulrasoul Khadim },
title = { Real-Time Safety Automobile Driver System },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 17 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number17/23304-2015906803/ },
doi = { 10.5120/ijca2015906803 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:25:55.641904+05:30
%A Azmi Shawkat Abdulbaki
%A Samir Abdulrasoul Khadim
%T Real-Time Safety Automobile Driver System
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 17
%P 34-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Driver Fatigue Detection (DFD) plays an important role in automobile safety and security. Driver fatigue is one of the leading causes of traffic accidents. Long-vehicles driving are needed to keep drivers under monitoring due to fatigue and more accidents. This monitoring include : eyes blinking , face expressions. Without these monitoring, the accidents is increasing and caused the driver death. Driver’s tiredness and drowsiness are the major causes of traffic accidents on road. This proposed prototype is monitor the driver's fatigue level and analyze fatigue reason's (tiredness, drowse or a drunken ) and real-time DFD online. The proposed model depend on eye gestures (eyes blinking) and sensors such as an infrared camera (camera remotely) to detect driver expression. These sensors directly pointed towards the driver’s face. This technology is not interactive with an outside driving situation .The system consist of Mesh network equipped Zigbee protocol , sensors and Xbee tag. The system analyze the variation of driver's eyes movement rate. According that parameters , the system can specify the level of driver’s fatigue based on the response signals and alert driver. Practically. This system is robust, reliable, and accurate to detect fatigue levels.

References
  1. Saranummi, N. In the spotlight "Health information systems". IEEE Rev. Biomed. Eng. 2008, 1, 15–17.
  2. Goldschmidt, P.G. HIT and MIS: "Implications of health information technology and medical information systems". Communication. ACM 2005, 48, 69–74.
  3. C. Jiangwei, J. Lisheng, T. Bingliang, S. Shuming, W. Rongben. 2004. "A monitoring method of driver mouth behavior based on machine vision". Proceedings of IEEE Intelligent Vehicles Symposium, Parma, Italy.
  4. S. Ribarić, J. Lovrenčić, and N. Pavešić, “A Neural-Network-Based System for Monitoring Driver Fatigue”, in Proc. of the 15th IEEE Mediterranean Electrotechnical Conference (MELECON), pp. 1356- 1361, 2010.
  5. S. Lal, A. Craig, P. Boord, L. Kirkup, and H. Nguyen, “Development of an algorithm for an eeg-based driver fatigue countermeasure,” Journal of Safety Research, vol. 1, no. 34, pp. 321–328, Febuary 2003.
  6. Q. Ji, Z. Zhu, and P. Lan, “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE transactions on vehicular technology, vol. 53, no. 4, pp. 1052–1068, July 2004.
  7. T. Wang, P. Shi. (2005). "Yawning detection for determining driver drowsiness". Proceedings of IEEE Intl. Workshop VLSI Design and Video Technology, China.
  8. L.M. Bergasa, J. Nuevo, M.A. Sotalo, and M. Vazquez, “Real-time system for monitoring driver Vigilance,” in Proc. Intelligent Vehicle Symp., Parma, Italy, pp.78-83, 2004.
  9. J. Nesvadba, A. Hanjalic, P. Fonseca, B. Kroon, H. Celik, E. Hendriks, ‘Towards a real-time and distributed system for face detection, pose estimation and face-related features’, Invited Paper, Proc. Int. Con. on Methods and Techniques in Behavioral Research, Wageningen, The Netherlands, 2005.
  10. K.Subhashini and Y. Bahindwar, "A Dedicated System for Monitoring of Driver’s Fatigue", International Journal of Innovative Research in Science, Engineering and Technology Vol. 1, Issue 2, December 2012.
  11. V. Ratanavaraha and D. Watthanaklang,"Road Safety Audit: Identification of Bus Hazardous Location in Thailand", IJST Indian international journal.
  12. Aryuanto and F. Yudi Limpraptono ," A Vision Based System for monitoring driver fatigue" A vision-based system for monitoring driver fatigue" ,Yogyakarta, 14 November 2009.
  13. R. Coetzer," Driver fatigue detection based on eye tracking", Department of Electrical, Electronic and Computer Engineering. University of Pretoria, Pretoria.
  14. Qiang Ji1 and Xiaojie Yangthis ," Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance", Real-Time Imaging 8, 357–377 (2002), available online at http://www.idealibrary.com.
  15. Pavan Patidar, Manoj Gupta, Sumit, Ashok, “Image Denoising By Various Filters For Different Noises”, International Journal of Computer Applications (0975 –8887)Volume 9–No.4, November 2010.
  16. Hawlader Abdullah Al-Mamun, Nadim Jahangir, Md. Shahedul Islam and Md. Ashraful Islam(2009), "Eye Detection in Facial Image by Genetic Algorithm Driven Deformable Template Matching", IJCSNS International Journal of Computer Science and Network Security, Vol 9, Issue 8, August 2009, Page(s): 287-294.
  17. Jie Tang, Zuhua Fang, Shifeng Hu, Ying Sun, (2010), “Driver Fatigue Detection Algorithm Based on Eye Features,” IEEE proceedings of 2010 Seventh International Conference on “Fuzzy Systems and Knowledge Discovery” (FSKD 2010).
  18. Wenhui Dong, Xiuojuan Wu, (2005), “Driver Fatigue Detection Based on the Distance of Eyelid,” IEEE Int. Workshop on “VLSI Design & Video Tech.”, Issue May 28-30,2005, Page(s):365-368.
  19. Wen-Bing Horng, Chih-Yuan Chen, Yi Chang, Chuu-Hai Fan, (2004), "Driver Fatigue Detection Based on Eye Tracking and Dynamic Template Matching", IEEE Proceedings of International Conference on ―Networking, Sensing & Control, Taipei, Taiwan.
  20. Xiao Fan Bao-Cai Yin Yan-Feng Sun “Yawning Detection for Monitoring Driver Fatigue” IEEE Machine Learning and Cybernetics, 2007 International Conference on 19-22 Aug. 2007.
  21. M. Divjak and H. Bischof, “Eye blink based fatigue detection for prevention of computer vision syndrome,” in the IAPR Conference on Machine Vision Applications, 2009, pp. 350–353.
  22. Tamilselvan, G.M. and A. Shanmugam. Multi hopping effect of Zigbee nodes coexisting with WLAN nodes in heterogeneous network environment in Cognitive Wireless Systems (UKIWCWS), 2009 First UK-India International Workshop on. 2009.
  23. Dissanayake, S.D., et al. Zigbee Wireless Vehicular Identification and Authentication System. in Information and Automation for Sustainability, 2008. ICIAFS 2008. 4th International Conference on. 2008.
  24. Zeghdoud, M., P. Cordier, and M. Terre. Impact of Clear Channel Assessment Mode on the Performance of ZigBee Operating in a WiFi Environment. in Operator-Assisted (Wireless Mesh) Community Networks, 2006 1st Workshop on. 2006.
  25. Dissanayake, S.D., et al. "Zigbee Wireless Vehicular Identification and Authentication System in Information and Automation for Sustainability", 2008. ICIAFS 2008. 4th International Conference on. 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Driver Fatigue Detection Eyes Blinking E-Safety.