A System on Intelligent Driver Drowsiness Detection Method

  • Authors

    • Ugra Mohan Kumar
    • Devendra Singh
    • Sudhir Jugran
    • Pankaj Punia
    • Vinay Negi
    2018-06-25
    https://doi.org/10.14419/ijet.v7i3.4.16765
  • Driver fatigue detection, Neural network, Alert, Advanced driver assistance systems.
  • We actualized a fatigue driver recognition framework utilizing a mix of driver's state and driving conduct pointers. For driver's express, the framework observed the eyes' blinking rate and the flickering span. Fatigue drivers have these qualities higher than ordinary levels. We utilized a camera with machine vision procedures to find out and watch driver's blinking behavior. Harr's feature classifier was utilized to first find the eye's range, and once found, a layout coordinating was utilized to track the eye for fast preparing. For driving conduct, we gained the vehicle's state from inertial measurement unit and gas pedal sensors. The principle component analysis was utilized to choose the components that have high change. The difference esteems were utilized to separate weakness drivers, which are accepted to have higher driving exercises, from typical drivers.

     

     

  • References

    1. [1] Yadav, V., "Driver Drowsiness Detection System", in IJCA Proceedings on International Conference on Intuitive Systems and Solutions 2012. 2012. Foundation of Computer Science (FCS).

      [2] Alshaqaqi, B., A.S. Baquhaizel, and M.E.A. Ouis, "Driver Drowsiness Detection System" Advances in Systems Science and Applications, 2016. 16(2): p. 94-102.

      [3] Alshaqaqi, B., "Driver drowsiness detection system. in Systems", Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on. 2013. IEEE.

      [4] Ambekar, S.N., "Driver Drowsiness Detection System" International Journal, 2016. 1(9).

      [5] Nivrutti, A.S.,"Driver Drowsiness Detection System", 2017.

      [6] Grace, R., "A drowsy driver detection system for heavy vehicles" in Digital Avionics Systems Conference, 1998. Proceedings., 17th DASC. The AIAA/IEEE/SAE. 1998. IEEE.

      [7] Kumar, S., "Fast and Efficient Medical Image Compression Using Contourlet Transform:(FEMI-CCT)" Open Journal of Computer Sciences, 2013. 1(1): p. 7-13.

      [8] Ueno, H., M. Kaneda, and M. Tsukino, "Development of drowsiness detection system", in Vehicle Navigation and Information Systems Conference, 1994. Proceedings., 1994. 1994. IEEE.

      [9] Singh, V., "3D reconstruction of ATFL ligament using ultrasound images", in Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference on. 2014. IEEE.

      [10] Sahoo, C., "Driver Drowsiness Detection System" 2016.

      [11] Singh, V., "Automatic ultrasound image segmentation framework based on darwinian particle swarm optimization" in Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. 2015. Springer.

      [12] Ji, Q. and X. Yang, "Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging", 2002. 8(5): p. 357-377.

      [13] Singh, V., et al., "Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images",Biomedical engineering online, 2016. 15(1): p. 13.

      [14] Hu, S. and G. Zheng, "Driver drowsiness detection with eyelid related parameters by Support Vector Machine"Expert Systems with Applications, 2009. 36(4): p. 7651-7658.

      [15] Singh, V., "Computer aided diagnosis (CAD) tool for the analysis of calcaneofibular ligament using ultrasonographic images" in Malaysian Technical Universities Conference on Engineering and Technology. 2015.

      [16] Wierwille, W.W., "Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness" final report. 1994.

      [17] Eskandarian, A. and A. Mortazavi, "Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection" in Intelligent Vehicles Symposium, 2007 IEEE. 2007. IEEE.

      [18] Anandan, P. and R. Sabeenian, "Image Compression Techniques using Curvelet, Contourlet, Ridgelet and Wavelet Transforms–A Review"Biometrics and Bioinformatics, 2013. 5(7): p. 267-270.

      [19] Hong, T., H. Qin, and Q. Sun, "An improved real time eye state identification system in driver drowsiness detection", in Control and Automation 2007. ICCA 2007. IEEE International Conference on. 2007. IEEE.

      [20] Singh, V., "3D reconstruction of CFL ligament based on ultrasonographic images" in International Visual Informatics Conference. 2015. Springer.

      [21] Sahayadhas, A., K. Sundaraj, and M. Murugappan, "Detecting driver drowsiness based on sensors: a review"Sensors, 2012. 12(12): p. 16937-16953.

      [22] Jo, J., "Vision-based method for detecting driver drowsiness and distraction in driver monitoring system",Optical Engineering, 2011. 50(12): p. 127202-127202-24.

      [23] Chen, C.-S., J. Lu, and K.-K. Ma, Computer Vision–ACCV 2016 Workshops: ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers. Vol. 10118. 2017: Springer.

      [24] Wong, W., "Effects of errorless learning on the acquisition of velopharyngeal movement control"Journal of the Acoustical Society of America, 2012.

      [25] Patel, M., "Applying neural network analysis on heart rate variability data to assess driver fatigue"Expert systems with Applications, 2011. 38(6): p. 7235-7242.

      [26] King, L., H.T. Nguyen, and S. Lal, "Early driver fatigue detection from electroencephalography signals using artificial neural networks", in Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE. 2006. IEEE.

      [27] Yeo, M.V., "Can SVM be used for automatic EEG detection of drowsiness during car driving?",Safety Science, 2009. 47(1): p. 115-124.

      [28] Lin, C.-T., "EEG-based drowsiness estimation for safety driving using independent component analysis",IEEE Transactions on Circuits and Systems I: Regular Papers, 2005. 52(12): p. 2726-2738.

      [29] Quang N. NguyenLe T. Anh ThoToi Vo VanHui YuNguyen DucThang, “Visual Based Drowsiness Detection Using Facial Featuresâ€, 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6) pp 723-727, BME 2017.

      [30] V Triyanti, H Iridiastadi, “Challenges in detecting drowsiness based on driver's behaviorâ€, IOP Conference Series: Materials Science and Engineering, Volume 277, 2017.

      [31] Kunika Chhaganbhai Patel, Shafiullah Atiullah Khan, Vijaykumar Nandkumar Patil, “Real-Time Driver Drowsiness Detection System Based on VisualInformationâ€, International Journal of Engineering Science and Computing, March 2018.

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    Mohan Kumar, U., Singh, D., Jugran, S., Punia, P., & Negi, V. (2018). A System on Intelligent Driver Drowsiness Detection Method. International Journal of Engineering & Technology, 7(3.4), 160-162. https://doi.org/10.14419/ijet.v7i3.4.16765