Using Image Processing in the Proposed Drowsiness Detection System Design

  • Mohsen POURSADEGHIYAN Research Center in Emergency and Disaster Health, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran Psychosis Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
  • Adel MAZLOUMI Dept. of Occupational Health, School of Public Health, International Campus, Tehran University of Medical Sciences, Tehran, Iran
  • Gebraeil NASL SARAJI Dept. of Occupational Health, School of Public Health, International Campus, Tehran University of Medical Sciences, Tehran, Iran
  • Mohammad Mehdi BANESHI Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
  • Alireza KHAMMAR Dept. of Occupational Health Engineering, School of Health, Zabol University of Medical Sciences, Zabol, Iran
  • Mohammad Hossein EBRAHIMI Occupational and Environmental Health Research Center, Shahroud University of Medical Sciences, Shahroud, Iran
Keywords: Driver drowsiness, Facial expression, Simulation driving, Road safety

Abstract

Abstract Background: Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of drowsiness, techniques based on image processing are quicker and more accurate in comparison with the other methods. The aim of this study was to use image-processing techniques to detect the levels of drowsiness in a driving simulator. Methods: This study was conducted on five suburban drivers using a driving simulator based on virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015 Tehran, Iran. The facial expressions, as well as location of the eyes, were detected by Violla-Jones algorithm. Criteria for detecting drivers’ levels of drowsiness by eyes tracking included eye blink duration blink frequency and PERCLOS that was used to confirm the results. Results: Eye closure duration and blink frequency have a direct ratio of drivers’ levels of drowsiness. The mean of squares of errors for data trained by the network and data into the network for testing, were 0.0623 and 0.0700, respectively. Meanwhile, the percentage of accuracy of detecting system was 93. Conclusion: The results showed several dynamic changes of the eyes during the periods of drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers’ drowsiness by considering the dynamic changes of the eyes.  

References

WHO (2009).Global Status Report on Road Safety Geneva, Switzerland. http://www.who.int/violence_injury_prevention/road_safety_status/report/cover_and_front_matter_en.pdf

Rau P (2005). Drowsy Driver Detection and Warning System for Commercial Vehicle Drivers: Field Operational Test Design, Analysis, and Progress. National Highway Traffic Safety Administration. Washington, DC, USA. https://www-nrd.nhtsa.dot.gov/pdf/esv/esv19/05-0192-W.pdf

Drivers Beware Getting Enough Sleep Can Save Your Life This Memorial Day. National Sleep Foundation (NSF); Arlington, VA, USA: 2010.

Husar P (2012). Eyetracker Warns against Momentary Driver Drowsiness. http://www.fraunhofer.de/en/press/research-news/2010/10/eye-tracker-driver-drowsiness.html

Poursadeghiyan M, Mazloumi 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

Karchani M, Mazloumi A, Saraji GN et al (2015). Relationship between Subjective Sleepiness and Demographic Characteristics in Night Work Drivers. Advances in Environmental Biology, 9(3):1012-1015

Ying Y, Jing S, Wei Z (2007). The Monitoring Method of Driver’s Fatigue Based on Neural Network. Proc. International Conf. on Mechatronics and Automation, China. pp.3555-9.

Lopar M, Ribarić S (2013). An Overview and Evaluation of Various Face and Eyes Detection Algorithms for Driver Fatigue Monitoring Systems. Proceedings of the Croatian Computer Vision Workshop, Year 1. Zagreb, Coroatia, pp.15-18

https://arxiv.org/ftp/arxiv/papers/1310/1310.0317.pdf

Khan MI, Mansoor AB (2008). Real Time Eyes Tracking and Classification for Driver Fatigue Detection, ICIAR 2008, LNCS 5112, Image Analysis and Recognition, pp: 729-738

Bergasa LM, Nuevo J, Sotelo MA, Barea R, Lopez ME (2006). Real-Time system for monitoring driver vigilance.IEEE trans Intell Transp Syst, 7 (1): 63-77.

Liu D, Sun P, Xiao YQ, Yin Y (2010). Drowsiness Detection Based on Eyelid Movement. Proc. the 2nd International Workshop on Education Technology and Computer Science (ETCS), Wuhan, China, 12–13; pp. 49–52.

Karchani M, Mazloumi A, NaslSaraji G et al (2015). Association of Subjective and Interpretive Drowsiness with Facial Dynamic Changes In Simulator Driving. J Res Health Sci, 15(4): 250-55.

Khammar A, Moghimian M, Ebrahimi MH et al (2017). Effects of Bright light shock on sleepiness and adaptation among night workers of a hospital in Iran. Ann Trop Med Public Health,10(3):595-599.

Omidianidost A, Hosseini S, Jabari M et al (2016). The Relationship Between Individual, Occupational Factors And LBP (Low Back Pain) In One Of The Auto Parts Manufacturing Workshops Of Tehran In 2015. J Eng Appl Sci, 11(5):1074-77.

Biglari H, Ebrahimi MH, Salehi M et al (2016). Relationship of Occupational Stress to Cardiovascular Disease Risk Factors in Drivers. Int J Occup Environ Health, 29(6): 895-901.

Azrah K, Poursadeghiyan M, Fani MJ et al (2016). Predicting health risks of exposure to whole body vibration in the urban taxi drivers. JHSW, 6 (3) :59-72.

Karchani M, Kakooei H, Yazdi Z, Zare M (2011). Do bright-light shock exposures during breaks reduce subjective sleepiness in night workers? Sleep Biol Rhythms 9(2): 95-102.

Craig A, Tran Y, Wijesuriya N, Boord PA (2006). A controlled Investigation in to the Psychological Determinants of Fatigue. Biol Psychol, 72 (1): 78-87.

Viola P, MJ JONES (2004). Robust real-time face detection. Int J Comput Vis, 57(2):137–54.

Singh S, Papanikolopoulos N.P (1999). Monitoring driver fatigue using facial analysis techniques, in Proc. Int. Conf. Intelligent Transportation Systems, Tokyo, Japan. pp. 314–318.

Rongben W, Lie G, Bingliang T, Lisheng J (2004). Monitoring Mouth Movement for Driver Fatigue or Distraction with One Camera, Proc. IEEE intelligent Transportation Systems Conference Washington, D.C., USA.

Ryan WJ, Duchowski AT, Birchfield ST (2008). Limbus/Pupil Switching For Wearable Eye Tracking Under Variable Lighting Conditions, Proc. ETRA, symposium on Eye tracking research & applications Savannah, Georgia, pp.61-64

Fan X, Yin BC, Sun YF (2009). Yawning detection based on gabor wavelets and LDA. J Beijing Univ Technol, 35(3): 409-413.

Yin BC, Fan X, SunYF (2009). Multiscale dynamic features based driver fatigue detection. Intern J Pattern Recognit Artif Intell, 23(3): 575-89.

Vural E, Cetin M, Ercil A et al (2007). Drowsy Driver Detection through Facial Movement Analysis. International Workshop on Human-Computer Interaction HCI 2007: Human–Computer Interaction pp 6-18.

Dong W, Wu X (2005). Driver Fatigue Detection Based on the Distance of Eyelid. Proc. IEEE Int. Workshop VLSI Design & Video Tech Suzhou China. pp. 365-368.

Published
2018-08-29
How to Cite
1.
POURSADEGHIYAN M, MAZLOUMI A, NASL SARAJI G, BANESHI MM, KHAMMAR A, EBRAHIMI MH. Using Image Processing in the Proposed Drowsiness Detection System Design. IJPH. 47(9):1370-7.
Section
Original Article(s)