International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 14, No. 2, 8 Apr. 2024
Cover page and Table of Contents: PDF (size: 812KB)
A Real-time Light-weight Computer Vision Application for Driver’s Drowsiness Detection
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Author(s)
Index Terms
Drowsiness detection, computer vision, Dlib, OpenCV, ear, mar, head tilt angle
Abstract
The issue of drowsiness while operating a motor vehicle is an increasingly common occurrence that has been found to contribute significantly to a substantial number of fatal accidents annually. The urgency of the current situation necessitates implementing a solution to mitigate accidents and fatalities. The present study aims to investigate a less intricate and less expensive but remarkably efficient approach for detecting drowsiness in drivers, in contrast to the existing complex systems developed for this purpose. This paper focuses on developing a simple drowsy driver detection system utilizing the Python programming language and integrating the OpenCV and Dlib models. The shape detector provided by Dlib is employed to accurately determine the spatial coordinates of the facial landmarks within the given video input. This enables the detection of drowsiness by monitoring various factors such as the aspect ratios of the eyes, mouth, and the angle of head tilt. The performance evaluation of the system under consideration is conducted through the utilization of standardized public datasets and real-time video footage. When tested with dataset image inputs, the system showed exceptional recognition accuracy. The performance comparison is done to show the efficacy of the proposed approach. Traveling can be made safer and more effective by combining the proposed system with additional safety features and automation technology in cars.
Cite This Paper
Saikat Baul, Md. Ratan Rana, Farzana Bente Alam, "A Real-time Light-weight Computer Vision Application for Driver’s Drowsiness Detection", International Journal of Engineering and Manufacturing (IJEM), Vol.14, No.2, pp. 23-33, 2024. DOI:10.5815/ijem.2024.02.02
Reference
[1]K.Sakthidasan Sankaran, N. Vasudevan, and V. Nagarajan. Driver Drowsiness Detection using Percentage Eye Closure Method. In 2020 International Conference on Communication and Signal Processing (ICCSP), pages 1422–1425, July 2020.
[2]Samsur Rahman. Road accidents kill highest number of students in 2022, January 2023. Available: https://en.prothomalo.com/bangladesh/accident/nr3n0krxfx
[3]Mohammad Mahbub Alam Talukder, Md Shahidul Islam, Ishtiaque Ahmed, and Md Asif Raihan. Causes of Truck and Cargo DriversˆaC™ Fatigue in Bangladesh. Jurnal Teknologi, 65(3), October 2013. Number: 3.
[4]Madaripur crash: Exhaustion from driving 33 hours led to accident. Available: https://www.dhakatribune.com/nation/2023/03/20/madaripur-crash-exhaustion-from-driving-33-hours-led-to-accident
[5]Mehrdad Sabet, Reza A. Zoroofi, Khosro Sadeghniiat-Haghighi, and Maryam Sabbaghian. A new system for driver drowsiness and distraction detection. In 20th Iranian Conference on Electrical Engineering (ICEE2012), pages 1247–1251, May 2012. ISSN: 2164-7054.
[6]Antoine Picot, Sylvie Charbonnier, and Alice Caplier. Drowsiness detection based on visual signs: blinking analysis based on high frame rate video. In 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings, pages 801–804, May 2010. ISSN: 1091-5281.
[7]Jay D. Fuletra and Dulari Bosamiya. A Survey on Drivers Drowsiness Detection Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 1(11):816–819, November 2013. Number: 11.
[8]Yong Du, Peijun Ma, Xiaohong Su, and Yingjun Zhang. Driver Fatigue Detection based on Eye State Analysis. pages 132–137. Atlantis Press, December 2008. ISSN: 1951-6851.
[9]Shinfeng D. Lin, Jia-Jen Lin, and Chin-Yao Chung. Sleepy Eye’s Recognition for Drowsiness Detection. In 2013 International Symposium on Biometrics and Security Technologies, pages 176–179, July 2013.
[10]Feng You, Xiaolong Li, Yunbo Gong, Hailwei Wang, and Hongyi Li. A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration. IEEE Access, 7:179396–179408, 2019.
[11]Ruben Florez, Facundo Palomino-Quispe, Roger Jesus Coaquira-Castillo, Julio Cesar Herrera-Levano, Thuanne Paixão, and Ana Beatriz Alvarez. A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification. Applied Sciences, 13(13):7849, January 2023. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute.
[12]Jafirul Islam Jewel, Md. Mahabub Hossain, and Md. Dulal Haque. Design and Implementation of a Drowsiness Detection System Up to Extended Head Angle Using FaceMesh Machine Learning Solution. In Md. Shahriare Satu, Mohammad Ali Moni, M. Shamim Kaiser, and Mohammad Shamsul Arefin, editors, Machine Intelligence and Emerging Technologies, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pages 79–90, Cham, 2023. Springer Nature Switzerland.
[13]Khuzaimah Rabiah Mahamad Khariol Nizar and Mohamad Hairol Jabbar. Driver Drowsiness Detection with an Alarm System using a Webcam. Evolution in Electrical and Electronic Engineering, 4(1):87–96, May 2023. Number: 1.
[14]Nageshwar Nath Pandey and Naresh Babu Muppalaneni. Real-Time Drowsiness Identification based on Eye State Analysis. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pages 1182–1187, March 2021.
[15]Tasawor Ahmed Sofi and Shabana Mehfuz. Drowsiness and fatigue detection using multi-feature fusion. In 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), pages 672–675, May 2023.
[16]Shruti Mohanty, Shruti V Hegde, Supriya Prasad, and J. Manikandan. Design of Real-time Drowsiness Detection System using Dlib. In 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pages 1–4, November 2019.
[17]Jithina Jose, J S Vimali, P Ajitha, S Gowri, A Sivasangari, and Bevish Jinila. Drowsiness Detection System for Drivers Using Image Processing Technique. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), pages 1527–1530, June 2021.
[18]Wanghua Deng and Ruoxue Wu. Real-Time Driver-Drowsiness Detection System Using Facial Features. IEEE Access, 7:118727–118738, 2019.
[19]Burcu Kır Savas¸ and Yas¸ar Becerikli. Real Time Driver Fatigue Detection Based on SVM Algorithm. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT), pages 1–4, October 2018.
[20]Rateb Jabbar, Khalifa Al-Khalifa, Mohamed Kharbeche, Wael Alhajyaseen, Mohsen Jafari, and Shan Jiang. Realtime Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques. Procedia Computer Science, 130:400–407, January 2018.
[21]Vandna Saini and Rekha Saini. Driver drowsiness detection system and techniques: a review. International Journal of Computer Science and Information Technologies, 5(3):4245–4249, 2014.
[22]Davis E. King. davisking/dlib-models, March 2024. original-date: 2015-09-22T00:30:07Z. Available: https://github.com/davisking/dlib-models.
[23]Pavel Korshunov and Sébastien Marcel. Speaker Inconsistency Detection in Tampered Video. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 2375–2379, September 2018. ISSN: 2076-1465.
[24]N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893, San Diego, CA, USA, 2005. IEEE.
[25]Shabnam Abtahi, Mona Omidyeganeh, Shervin Shirmohammadi, and Behnoosh Hariri. YawDD: a yawning detection dataset. In Proceedings of the 5th ACM Multimedia Systems Conference, MMSys ’14, pages 24–28, New York, NY, USA, March 2014. Association for Computing Machinery.