SISTEM DETEKSI KELELAHAN PENGEMUDI BERDASARKAN PENGUKURAN KEDIPAN MATA

  • Ressa Ardiansya Putra Universitas 17 Agustus 1945 Surabaya
  • Fajar Astuti Hermawati Universitas 17 Agustus 1945 Surabaya
DOI: https://doi.org/10.30996/konv.v13i2.2755

Abstract

Pendeteksian kelelahan pada pengemudi sangatlah penting untuk keamanan dalam berkendara, terutama pada saat perjalanan jauh atau mudik sekalipun. Penelitian ini mengajukan sebuah sistem deteksi kelelahan pengemudi berbasis video berdasarkan pada pengukuran kedipan mata (eye blinking detection). Data yang digunakan direkam menggunakan kamera smartphone dengan posisi pengemudi menghadap kamera (frontal face). Proses penentuan kondisi pengemudi dimulai dengan pendeteksian lokasi wajah dari pengemudi menggunakan metode Viola-Jones. Jika wajah sudah terdeteksi, dilanjutkan dengan deteksi mata pada citra wajah tersebut menggunakan metode yang sama. Hasil deteksi mata ini digunakan untuk menentukan kondisi mata terbuka atau mata tertutup. Deteksi dilakukan pada setiap frame gambar video.  Seorang pengemudi dikatakan berada pada kondisi kelelahan atau mengantuk jika mata tertutup selama minimal 9 frame berturutan atau selama 0.3 detik. Dari hasil pengujian, wilayah mata terdeteksi dengan cukup baik dengan akurasi rata-rata dari hasil deteksi kedipan mata yaitu kondisi mata tertutup atau terbuka adalah sebesar 98.73%.

 

Kata Kunci: Pengenalan wajah, pengenalan mata, deteksi kelelahan pengemudi, deteksi kedipan mata (eye blinking detection), sistem transportasi cerdas

Downloads

Download data is not yet available.

References

F. A. Hermawati, G. Kusnanto, and E. Sadewa, “Pengenalan Lokasi Plat Nomor Kendaraan Indonesia dengan Transformasi Fourier,†in National Conference on Computer Science & Information Technology, 2007, pp. 389–393.

F. A. Hermawati and R. Koesdijarto, “A Real-Time License Plate Detection System,†TELKOMNIKA: Indonesian Journal of Electrical Engineering, vol. 8, no. 1, pp. 97–106, 2010.

F. A. Hermawati and H. Budianto, “A Video Based License Plate Detection System Using Viola-Jones Method,†in Proc. 2nd SciTech Internasional Seminar, 2013, pp. 63–69.

F. A. Hermawati and N. Sholeh, “Pengenalan Rambu Batas Kecepatan Pada Sebuah Citra Dengan Metode Template Matching,†KONVERGENSI, vol. 6, no. 1, pp. 1–8, 2010.

J. H. Yang, Z. Mao, L. Tijerina, T. Pilutti, J. F. Coughlin, and E. Feron, “Detection of Driver Fatigue Caused by Sleep Deprivation,†IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, vol. 39, no. 4, pp. 694–705, 2009.

I. Teyeb, O. Jemai, M. Zaied, and C. Ben Amar, “Towards a Smart Car Seat Design for Drowsiness Detection Based on Pressure Distribution of the Driver ’ s Body,†in ICSEA 2016 : The Eleventh International Conference on Software Engineering Advances, 2016, pp. 217–222.

H. Yin, Y. Su, Y. Liu, and D. Zhao, “A Driver Fatigue Detection Method Based on Multi-Sensor Signals,†in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.

W. Kong, L. Zhou, Y. Wang, J. Zhang, J. Liu, and S. Gao, “A System of Driving Fatigue Detection Based on Machine Vision and Its Application on Smart Device,†Journal of Sensors, vol. 2015, pp. 1–11, 2015.

S. S. Bharambe and P. M. Mahajan, “Implementation of Real Time Driver Drowsiness Detection System,†International Journal of Science and Research (IJSR), vol. 4, no. 1, pp. 2202–2206, 2015.

Y. Chellappa, N. N. Joshi, and V. Bharadwaj, “Driver Fatigue Detection System,†in 2016 IEEE International Conference on Signal and Image Processing, 2016, pp. 655–660.

N. Kuamr and N. C. Barwar, “Analysis of Real Time Driver Fatigue Detection Based on Eye and Yawning,†International Journal of Computer Science and Information Technologies, vol. 5, no. 6, pp. 7821–7826, 2014.

H. Gu and Q. Ji, “An Automated Face Reader for Fatigue Detection,†in The Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR’04), 2004, pp. 1–6.

R. Lienhart, A. Kuranov, and V. Pisarevsky, “Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection,†in Pattern Recognition. 25th DAGM Symposium, Magdeburg, Germany, September 10-12, 2003, Proceedings, B. Michaelis and G. Krell, Eds. Springer-Verlag Berlin Heidelberg, 2003, pp. 297–304.

C. Guerra, M. Herna, and M. Castrillo, “ENCARA2 : Real-time detection of multiple faces at different resolutions in video streams,†Journal of Visual Communication and Image Representation, vol. 18, no. 2, pp. 130–140, 2007.

Published
2019-09-02
Issue
Vol 13 No 2 (2017)
Section
Articles

Authors whose manuscript is published will approve the following provisions:

  1. The right to publication of all journal material published on the Konvergensi Teknologi Informasi & Komunikasi website is held by the editorial board with the author's knowledge (moral rights remain the property of the author).
  2. The formal legal provisions for access to digital articles of this electronic journal are subject to the terms of the Creative Commons Attribution-ShareAlike (CC BY-SA) license, which means Konvergensi Teknologi Informasi & Komunikasi reserves the right to store, modify the format, administer in database, maintain and publish articles without requesting permission from the Author as long as it keeps the Author's name as the owner of Copyright.
  3. Printed and electronic published manuscripts are open access for educational, research and library purposes. In addition to these objectives, the editorial board shall not be liable for violations of copyright law.