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Reseach Article

Using Mobile Platform to Detect and Alerts Driver Fatigue

by Maysoon F. Abulkhair, Hesham A. Salman, Lamiaa F. Ibrahim
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 8
Year of Publication: 2015
Authors: Maysoon F. Abulkhair, Hesham A. Salman, Lamiaa F. Ibrahim
10.5120/ijca2015905428

Maysoon F. Abulkhair, Hesham A. Salman, Lamiaa F. Ibrahim . Using Mobile Platform to Detect and Alerts Driver Fatigue. International Journal of Computer Applications. 123, 8 ( August 2015), 27-35. DOI=10.5120/ijca2015905428

@article{ 10.5120/ijca2015905428,
author = { Maysoon F. Abulkhair, Hesham A. Salman, Lamiaa F. Ibrahim },
title = { Using Mobile Platform to Detect and Alerts Driver Fatigue },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 8 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 27-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number8/21981-2015905428/ },
doi = { 10.5120/ijca2015905428 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:09.318981+05:30
%A Maysoon F. Abulkhair
%A Hesham A. Salman
%A Lamiaa F. Ibrahim
%T Using Mobile Platform to Detect and Alerts Driver Fatigue
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 8
%P 27-35
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

When driver is in the state of drowsiness he can cause accidents. This state is the state between being awake and asleep. In this state driver reaction time is slower, his attentiveness is reduced, and his information processing is less efficient. Driver Fatigue Detection System (called FDS) has been proposed by the authors in a recent work. The FDS aims to monitor the driver and the alertness to prevent them from falling asleep at the wheel. FDS is very hard to fix in a car. In the present paper, the FDS software is modified and new system WakeApp is developed to be run in smartphone instead of Laptop and use all advantages of smartphone like camera and late weight. The WakeApp will solve this problem by using a mobile phone camera; the phone will be put on a stand in the car to make the driver feels comfortable. The WakeApp has hardware and software components such as mobile camera and Android SDK. Both components are integrated together to record real video for the driver, and then processing it for real-time eye tracking. WakeApp has reserve all advantages in FDS like fast and real-time face and eye tracking, external illumination interference is limited, more robustness and accuracy allowance for fast head/face movement. The Main goals of WakeApp are to ensure that the driver is staying awake during his drive, make the driver feels comfortable and to help decrease the number of accidents.

References
  1. SangkyunP, Seonyoung L, Soojin K, Kyeongsoon C. 2011. Design of AdaBoost classifier circuit using Haar-like features for automobile applications. International SoC Design Conference (ISOCC) (17-18 Nov. 2011), pp: 262 – 265.
  2. Konstantin P. 2010. Statistics Related to Drowsy Driver Crashes, (7 Jul. 2010), www.americanindian.net.
  3. Sun Z, et al. 2006. On-road Vehicles Detection: A Review. IEEE Trans. On Pattern Analysis and Machine Intelligence, (May 2006), 28- 5: 649-711.
  4. Dollar P, et al. 2009. Pedestrian Detection a Benchmark. Proc. Of IEEE conference on Computer Vision and Pattern Recognition, (Jun 2009), 304-311.
  5. Rasolzadeh B, et al. 2006. Response Bining: Improved Weak Classifiers for Boosting. IEEE Intelligent Vehicles Symposium, 2006; 344-349.
  6. Viola P, et al. 2003. Detecting Pedestrians Using Patterns of Motion and Appearance. IEEE International Conference on Computer Vision, (Oct. 2003), 2: 734-741.
  7. Zheng W, Liang L. 2009. Fast Car Detection Using Image Strip Features. IEEE Conference on Computer Vision and Pattern Recognition, (Jun. 2009), 2703-2710.
  8. Sivaraman S, Trivedi M. 2009. Active Learning Based Robust Monocular Vehicle Detection for On-road Safety Systems. IEEE Intelligent Vehicles Symposium, 399-404.
  9. Coetzer R, Hancke G. Driver fatigue detection: A survey. IEEE AFRICON Conference; September 2009.
  10. Lal S, Craig A, Boord P, Kirkup L, and Nguyen H. Development of an algorithm for an eeg-based driver fatigue countermeasure. Journal of Safety Research; Feb. 2003; 1-34: 321–328.
  11. Seong K, Haet L , Jung K, Jae B, Suk B, Suk K. 2007. ECG, EOG detection from helmet based system. 6th International Special Topic Conference on Information Technology Applications in Biomedicine ITAB 2007, 191–193.
  12. Boyraz P, Hansen J. 2008. Active accident avoidance case study: integrating drowsiness monitoring system with lateral control and speed regulation in passenger vehicles, IEEE International Conference on Vehicular Electronics and Safety, ICVES 2008, 293–298
  13. Sayed R, Eskandarian A. 2001. Unobtrusive drowsiness detection by neural network learning of driver steering. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, (Jun 2001) 215-9: 969–975.
  14. Eskandarian A and Mortazavi A. 2007. Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection. Intelligent Vehicles Symposium, IEEE ( Jun 2007), 553–559.
  15. Breuer J. 2008. Attention assist: Don’t fall asleep!. Daimler, Tech. Rep (November 2008).
  16. Wierwille W, Ellsworth L, Wreggit S, Fairbanks R, and Kim C. 1994. Research on vehicle-based driver status/performance monitoring: development, validation and refinement of algorithms for detection of driver drowsiness. National highway traffic safety administration, 808- 247.
  17. Dinges D, Mallis M, and Powell J. 1998. Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management. Department of transport safety, (April 1998), 808-762.
  18. SmartEye 2010. Antisleep 2.0. Internet published whitepaper, [accessed November 2010].
  19. Seeing Machines. 2007. Driver state sensor. user manual 2.0.
  20. Tabrizi PR, Zoroofi RA. 2009. Drowsiness detection based on brightness and numeral features of eye image. Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing IIH-MSP'09, 1310– 1313
  21. Zhang Z, Zhang J S. 2006. Driver fatigue detection based intelligent vehicle control. The 18th IEEE International Conference on Pattern Recognition.
  22. Devi MS, Bajaj PR. 2008. Driver fatigue detection based on eye tracking. First International Conference on Emerging Trends in Engineering and Technology, 649–652
  23. Hong T, Qin H, Sun Q. 2007. An improved real time eye state identification system in driver drowsiness detection. IEEE International Conference on Control and Automation (May 2007), 0: 1449–1453
  24. Coetzer R C, Hancke G P. 2011. Eye detection for a real-time vehicle driver fatigue monitoring system. 2011 IEEE Intelligent Vehicles Symposium (IV) , (5-9 June 2011), 66 – 71
  25. Maysoon Abulkhair, Arwa H. Alsahli, Kawther M. Taleb, Atheer M. Bahran, Fatimah M. Alzahrani, Hend A. Alzahrani, Lamiaa Fattouh Ibrahim, 2015. Mobile Platform Detect and Alerts System for Driver Fatigue, The 2015 International Conference on Soft Computing and Software Engineering (SCSE 2015), Berkeley,California, USA, (March 5-6), 2015.
  26. Lamiaa F. Ibrahim,  Maysoon Abulkhair,  Amal D. AlShomrani,  Manal AL-Garni,  Ameeerah AL-Mutiry,  Fadiah AL-Gamdi,  Roaa’a Kalenen, 2014. Using Haar Classifiers to Detect Driver Fatigue and Provide Alerts. Multimedia tools and applications, Springer, 71-3: 1857-1877, DOI: 10.1007/s11042-012-1308-5.
  27. Wang X, Wang Z, Sun J, Zhang H. 2005. The correlation template matching algorithm based TD filter and ESO filter. International Conference on Machine Learning and Cybernetics, Guangzhou, China, 9:5361–5365
  28. Adam Kerin. 2014. Introducing the Snapdragon 810 and 808 Processors:The Ultimate Connected Computing Experience. www.qualcomm.com/media. [Online] (Apr. 2014).
  29. Qualcomm Technologies. Snapdragon SDK for Android. www.developer.qualcomm.com/mobile-development. [Online]
  30. Google. Mobile Courses, Android Development. www.developers.google.com. [Online] (Sep. 2012).
  31. D. Switkin , Senior Software Engineer, Google Inc. Android Application Development. www.moss.csc.ncsu.edu. [Online]
  32. Eclipse Org. Eclipse. www.help.eclipse.org. [Online]
  33. Android Developer. The Android SDK. www.developer.android.com. [Online]
  34. Android NDK. www.Developer.android.com. [Online]
  35. Wambler, scott. Agile Modeling. www.agileModeling.com. [Online]
  36. The Complete Mining Fatigue Monitoring system. www.ifatigue.com/iFatigue%20Fleet%20Monitoring%20System.pdf. [Online]
  37. "Accuracy." Business Dictionary. www.businessdictionary.com. [Online] (Feb. 2011).
Index Terms

Computer Science
Information Sciences

Keywords

FDS Android SDK WakeApp