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2019 | OriginalPaper | Buchkapitel

Vision-Based Driver’s Attention Monitoring System for Smart Vehicles

verfasst von : Lamia Alam, Mohammed Moshiul Hoque

Erschienen in: Intelligent Computing & Optimization

Verlag: Springer International Publishing

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Abstract

Recent studies revealed that the driver’s inattention is one of the most prominent reasons for car accidents. Intelligent driving assistant system with real time monitoring of the driver’s attentional status may reduce the accident rate that mostly occurred due to lack of attention. In this paper, we presents a vision-based driver’s attention monitoring system that estimates the driver’s attentional status in terms of four categories: attentive, distracted, drowsy, and fatigue respectively. The attentional status is classified with a variety of parameters such as, percentage of eyelid closure over time (PERCLOS), yawn frequency and gaze direction. Experimental results with different subjects show that the system can classify the driver’s attentional status with a reasonable accuracy.

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Vorheriges Kapitel CAIAS Simulator: Self-driving Vehicle Simulator for AI Research

Nächstes Kapitel Characterizing Current Features of Malicious Threats on Websites

Global Status Report on Road Safety 2015, World Health Organization, WHO Press, Switzerland. http://www.who.int/entity/violence_injury_prevention/road_safety_status/2015/en/index.html

Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., Ramsey, D.J.: The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data. Technical report, National Highway Traffic Safety Administration, Washington, DC, USA (2006)

Arun, S., Sundaraj, K., Murugappan, M.: Driver inattention detection methods: a review. In: IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT), pp. 1–6, Kuala Lumpur (2012)

De Valck, E., Cluydts, R.: Slow-release caffeine as a countermeasure to driver sleepiness induced by partial sleep deprivation. J. Sleep Res. 10, 203–209 (2001)CrossRef

Guo, Z., Pan, Y., Zhao, G., Cao, S., Zhang, J.: Detection of driver vigilance level using EEG signals and driving contexts. IEEE Trans. Reliab. 67(1), 370–380 (2018)CrossRef

Wang, H., Dragomir, A., Abbasi, N.I., Li, J., Thakor, N.V., Bezerianos, A.: A novel real-time driving fatigue detection system based on wireless dry EEG. Cogn. Neurodynamics, 1–12. Springer, Netherlands (2018)

Li, G., Chung, W.Y.: Combined EEG-Gyroscope-tDCS brain machine interface system for early management of driver drowsiness. IEEE Trans. Hum.-Mach. Syst. 48(1), 50–62 (2018)CrossRef

Schoiack, M. M. V.: Driver drowsiness detection and verification system and method. U.S. Patent 8,631,893 B2 (2014)

Shibli, A. M., Hoque, M. M., Alam, L.: Developing a vision-based driving assistance system. In: 2018 International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS), Kolkata, India (2018)

10.

Chien,J.-C., Chen, Y.-S., Lee, J.-D.: Improving night time driving safety using vision-based classification techniques. Sensors 17(10), 2199 (2017)CrossRef

11.

Mandal, B., Li, L., Wang, G.S., Lin, J.: Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans. Intell. Transp. Syst. 18(3), 545–557 (2017)CrossRef

12.

Chowdhury, P., Alam, L., Hoque, M. M.: Designing an empirical framework to estimate the driver’s attention. In: 5th International Conference on Informatics, Electronics & Vision (ICIEV), pp. 513–518, Dhaka, Bangladesh (2016)

13.

Vicente, F., Huang, Z., Xiong, X., Torre, F.D.I., Zhang, W., Levi, D.: Driver gaze tracking and eyes off the road detection system. IEEE Trans. Intell. Transp. Syst. 16(4), 2014–2027 (2015)CrossRef

14.

Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Computational Learning Theory, pp. 23–37. Springer, Heidelberg (1995)

15.

De, K.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)

16.

Martin, D., Häger, G., Khan, F.H., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014)

17.

Facial Point Annotations. https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/

18.

Soukupova, T., Cech, J.: Real-time eye blink detection using facial landmarks. In: Cehovin, L., Mandeljc, R., Struc, V. (eds.) 21st Computer Vision Winter Workshop. Rimske Toplice, Slovenia (2016)

19.

Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985)CrossRef

20.

Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8, 179–187 (1962)MATH

21.

Arbuck, D.: Is yawning a tool for wakefulness or for sleep? Open J. Psychiatry 3(1), 5–11 (2013)CrossRef

22.

Chang, F.-J., Tran, A.T., Hassner, T., Masi, I., Nevatia, R., Medioni, G.: FacePoseNet: making a case for landmark-free face alignment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1599–1608, Venice, Italy (2017)

Titel: Vision-Based Driver’s Attention Monitoring System for Smart Vehicles
verfasst von: Lamia Alam
Mohammed Moshiul Hoque
Verlag: Springer International Publishing
Buch: Intelligent Computing & Optimization
Print ISBN: 978-3-030-00978-6

Electronic ISBN: 978-3-030-00979-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-00979-3_20

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