Abstract
Due to the increasing demand of transportation, vehicles are operating almost non-stop, day and night. This generally creates a tiring schedule for the vehicle as well as for the drivers of those vehicles. A huge number of road accidents occur due to the fatigue of the drivers. Fatigue makes the person drowsy, and they need sleep. Here, a system is proposed to detect the drowsiness of the driver. The system consists a camera to be installed in front of the driver to capture the face of the driver. As a region of interest, first the face will be traced and then the eye of the driver. Now through a pre-trained CNN model, the eye that is closed or opened will be decided. If the time duration exceeds some threshold, then an alarm will be ringed. The CNN is trained with a data set of 7000 images of open and close eye in different light condition. The experiments performed with the system in different condition, like gender, day, night, etc., and with some volunteer drivers. The results are interesting and satisfactory. There could be lots of refinement on the system and a mature system can be generated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumar J, Roy A (2022) DograNet—a comprehensive offline Dogra handwriting character dataset. J Phys 2251:012008. https://doi.org/10.1088/1742-6596/2251/1/012008
Roy A, Ghosh D (2021) Pattern recognition based tasks and achievements on handwritten Bengali character recognition. In: 2021 6th International conference on inventive computation technologies (ICICT), Coimbatore, India, 2021. IEEE, pp 1260–1265. https://doi.org/10.1109/ICICT50816.2021.9358783
Roy A (2019) Handwritten Bengali character recognition—a study of works during current decade. Adv Appl Math Sci 18(9):867–875 (0974-6803)
Deshmukh S, Roy A (2022) Early detection of diabetic retinopathy using vessel segmentation based on deep neural network. In: Proceedings of international conference on recent advances in materials, manufacturing and machine learning (RAMMML-2022), Nagpur, 26–27 April 2022
Mushtaq S, Roy A, Teli TA (2021) A comparative study on various machine learning techniques for brain tumor detection using MRI. In: Global emerging innovation summit (GEIS-2021). Bentham Science Publishers, pp 125–137. https://doi.org/10.2174/97816810890101210101
Deshmukh SV, Roy A, An empirical exploration of artificial intelligence in medical domain for prediction and analysis of diabetic retinopathy: review. J Phys: Conf Ser 1831:012012. https://doi.org/10.1088/1742-6596/1831/1/012012
Wang L, Wu X, Yu M (2007) Review of driver fatigue/drowsiness detection methods. J Biomed Eng 24(1):245–248
Borghini G, Astolfi L, Vecchiato G, Mattia D, Babiloni F (2014) Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev 44:58–75
Myllylä T, Korhonen V, Vihriälä E et al (2012) Human heart pulse wave responses measured simultaneously at several sensor placements by two MR-compatible fibre optic methods. J Sens 2012:8p, Article ID 769613
Simon M, Schmidt EA, Kincses WE et al (2011) EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions. Clin Neurophysiol 122(6):1168–1178
Lal SKL, Craig A (2005) Reproducibility of the spectral components of the electroencephalogram during driver fatigue. Int J Psychophysiol 55(2):137–143
Jap BT, Lal S, Fischer P, Bekiaris E (2009) Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst Appl 36(2):2352–2359
Huang CX, Zhang WC, Huang CG, Zhong YJ (2008) Identification of driver state based on ARX in the automobile simulator. Technol Econ Areas Commun 10(2):60–63
Chinthalachervu R, Teja I, Ajay Kumar M, Sai Harshith N, Santosh Kumar T (2022) Driver drowsiness detection and monitoring system using machine learning. J Phys: Conf Ser 2325:012057. https://doi.org/10.1088/1742-6596/2325/1/012057
Haribabu J, Navya T, Praveena PV, Pavithra K, Sravani K (2022) Driver drowsiness detection using Machine Learning. J Eng Sci 13(06)
Prasath N, Sreemathy J, Vigneshwaran P (2022) Driver drowsiness detection using machine learning algorithm. In: 2022 8th International conference on advanced computing and communication systems (ICACCS), 2022, pp 01–05. https://doi.org/10.1109/ICACCS54159.2022.9785167
Cheerla S, Reddy DP, Raghavesh KS (2022) Driver drowsiness detection using machine learning algorithms. In: 2022 2nd International conference on artificial intelligence and signal processing (AISP), 2022, pp 1–6. https://doi.org/10.1109/AISP53593.2022.9760618
Al Redhaei A, Albadawi Y, Mohamed S, Alnoman A (2022) Realtime driver drowsiness detection using machine learning. In: 2022 Advances in science and engineering technology international conferences (ASET), 2022, pp 1–6. https://doi.org/10.1109/ASET53988.2022.9734801
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Roy, A., Ghosh, D. (2023). Real-Time Driver Drowsiness Detection System Using Machine Learning. In: Tripathi, A.K., Anand, D., Nagar, A.K. (eds) Proceedings of World Conference on Artificial Intelligence: Advances and Applications. WWCA 1997. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-5881-8_37
Download citation
DOI: https://doi.org/10.1007/978-981-99-5881-8_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5880-1
Online ISBN: 978-981-99-5881-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)