Original Article

Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi


Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver.

Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected.

Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%.

Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.

1. Poursadeghiyan M, Amjad RN, Baneshi MM, et al (2017). Drowsiness trend in night workers and adaptation to night shift in hospital staff. Ann Trop Med Public Health, 10(4) : 989.
2. Poursadeghiyan M, Adel MA, Saraji GN, et al (2018). Using Image Processing in the Proposed Drowsiness Detection System Design. Iran J Public Health, 47(9):1371-1378.
3. Poursadeghiyan M, Mazlaumi A, Saraji GN, et al (2017). Determination the Levels of Subjective and Observer Rating of Drowsiness and Their Associations with Facial Dynamic Changes. Iran J Public Health, 46(1): 93–102.
4. Vesselenyi T, Moca S, Rus A, et al (2017). Driver drowsiness detection using ANN image processing. IOP Conf. Ser.: Mater. Sci. Eng, pp. : 1 – 8.
5. Kholerdi HA, Nejad NT, Ghaderi R, Baleghi Y (2016). Driver's drowsiness detection using an enhanced image processing technique inspired by the human visual system. J Connect Sci, 28 (1) : 27 – 46.
6. Ming AL, Cheng Z, Jin FY (2010). An EEG-based Method For Detecting Drowsy Driving State. Proceeding of 7th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. : 2164-2167.
7. Garces CA, Laciar LE (2010). An Automatic Detector of Drowsiness Based on Spectral Analysis and Wavelet Decomposition of EEG Records. Proceeding of IEEE Anual International Conference of The Engineering in Medicine and Biology Society (EMBC), pp. : 1405-1408.
8. Vicente J, Laguna P, Bartra A, Bailon R (2011). Detection of Drivers' Drowsiness by Means of HRV Analysis. Computing in Cardiology 2011, Hangzhou, China, 18 – 21, pp. : 89-92.
9. Suryaprasad J, Sandesh D, Saraswathi V, et al (2013). Real-Time Drowsy Driver Detection Using Haarcascade Samples. Computer Science & Information Technology (CS & IT )CP, 45–54.
10. Ingre M, Akerstedt T, Peters B, et al (2006). Subjectives Sleepiness, Simulated Driving Performance and Blink Duration: Examining Individual Differences. J Sleep Re, 15(1) : 47-53.
11. Bergasa LM, Nuevo J, Sotelo MA, Barea R (2004). Real-time System for Monitoring Driver Vigilance.  IEEE Transactions on Intelligent Transportation Systems, 7:63-77.
12. Viola P, Jones M (2001). Rapid Object Detection Using Boosted Cascade of Simple Features. Proc. of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8-14, Kauai, Hawaii, USA, pp. : 1 – 9.
13. Adi K, Widodo AP, Widodo CE, et al (2019). Detecting driver drowsiness using total pixel algorithm. J Phys Conf Ser, 1 – 6.
14. Adi K, Widodo CE, Widodo AP, et al (2018). Detection lung cancer using Gray Level Co-Occurrence Matrix (GLCM) and back propagation neural network classification. J Eng Sci Technol Rev, 11(2) : 8-12
15. Gonzalez RC, Woods RE, Eddins SL (2009). Digital Image Processing using MATLAB. 2nd ed. Gatesmark Publishing, United Stated of America, pp.: 12 – 67.
16. Adi K, Suksmono AB, Mengko TLR, Gunawan H (2010). Phase unwrapping by Markov Chain Monte Carlo energy minimization. IEEE Geoscience and Remote Sensing Letters, 7(4) : 704-707.
17. Jain AK (1986). Fundamental of Digital Image Processing. Prentice Hall, United State of America, pp. 49 – 75.
18. Wilhelm B, Mark JB (2007). Digital Image Processing: An Algorithmic Approach Using Java. Springer-Verlag, New York, pp. : 199 – 234.
19. Revathy N, Guhan T (2012). Face recognition system using backpropagation artificial neural networks. Int J Adv Eng Technol, 3(1) : 321 – 324.
20. Krishna MG, Srinivasulu A (2012). Face detection system on AdaBoost algorithm using Haar classifiers. Int J Mod Eng Res, 2(5) : 3556 - 3560.
21. Lienhart R, Jochen M (2002). An extended set of haar-like features for rapid object detection. Proc. International Conference on Image Processing, 900-903.
22. Adelkhani A, Beheshti B, Minaei S, Javadikia P (2012). Optimization of Lighting Conditions and Camera Height for Citrus Image Processing. World Appl Sci J, 18 (10): 1435-1442.
23. Ozkaya YA, Acar M, Jackson MR (2005). Digital image processing and illumination techniques for yarn characterization. J Electron Image, 14(2): 1-13.
IssueVol 49 No 9 (2020) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijph.v49i9.4084
Lost focused driver Drowsiness detection Haar cascade classifier; Real-time Raspberry Pi

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
ADI K, WIDODO C, WIDODO A, ARISTIA H. Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi. Iran J Public Health. 2020;49(9):1675-1682.