nach oben

Machine Vision and Applications

Erschienen in:

01.01.2015 | Original Paper

Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration

verfasst von: Belhassen Akrout, Walid Mahdi

Erschienen in: Machine Vision and Applications | Ausgabe 1/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Driver fatigue is one of the leading causes of road accidents. It affects the mental vigilance of the driver and reduces his personal capacity to drive a vehicle in full safety. These factors increase the risk of human errors which could involve deaths and wounds. Consequently, the development of an automatic system, which controls the driver fatigue and prevents him from accidents in advance, has received a growing interest. In this work, we have proposed a fusion system for drowsiness detection based on blinking measurement and the 3D head pose estimation. We have studied the driver’s eye behaviors by analysing a non-stationary and non-linear signal and we estimate the head rotation in the three directions \(Yaw\), \(Pitch\), and \(Roll\) by exploiting only three interest points of the face. Our suggested system of fusion presents three levels of drowsiness: awake, tired, and very tired. This system is evaluated by both \(DEAP\) and \(MiraclHB\) databases. The evaluation shows many promising results and shows the effectiveness of the suggested approach.

Nächster Artikel A robust multilevel segment description for multi-class object recognition

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Horng, W.-B., Chen, C.-Y., Chang, Y., Fan, C.-H.: Driver fatigue detection based on eye tracking and dynamk template matching, IEEE International Conference on Networking, Sensing and Control 2004, pp. 7–12. IEEE Conference Publications, Taiwan (2004)

Singh, S., Papanikolopoulos, N.: Monitoring driver fatigue using facial analysis techniques, International Conference on Intelligent Transportation Systems. IEEE Conference Publications, Tokyo (1999)

Ueno, H., Kaneda, M., Tsukino, M.: Development of drowsiness detection system, Vehicle Navigation and Information Systems Conference. IEEE Conference Publications, Yokohama (1994)

Ma, H., Yang, Z., Song, Y., Jia, P.: A fast method for monitoring driver fatigue using monocular camera, Proceedings of the 11th Joint Conference on Information Sciences. Atlantis, China (2008)

Ji, Q., Zhu, Z., Lan, P.: Real time non-intrusive monitoring and prediction of driver fatigue. IEEE Trans. Veh. Technol. 53, 1052–1068 (2004)CrossRef

Du, Y., Ma, P., Su, X., Zhang, Y.: Driver fatigue detection based on eye state analysis, Proceedings of the 11th Joint Conference on Information Sciences. Atlantis, China (2008)

Johns, M., Tucker, A., Chapman, R., Crowley, K., Michael, N.: Monitoring eye and eyelid movements by infrared reflectance oculography to measure drowsiness in drivers. Somnologie Schlafforschung und Schlafmedizin 11, 234–242 (2007)CrossRef

Omi, T., Nagai, Fumiya., Komura, Takashi.: Driver drowsiness detection focused on eyelid behaviour, 34th Congress on Science and Technology of Thailand, pp. 1–8. Thailand (2008)

Picot, A., Charbonnier, S., Caplier, A., Vu, N.-S.: Using retina modelling to characterize blinking: comparison between EOG and video analysis. Mach. Vis. Appl. 23, 1195–1208 (2012)CrossRef

10.

Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features, IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518. IEEE Computer Society, USA (2001)

11.

Akrout, B., Mahdi, W.: Eye blinking analysis based approach for drowsy driver detection, 5th International Conference on Web and Information Technologies, pp. 235–244. Hammamet, Tunisia (2013)

12.

Allen, J.G., Xu, R.Y.D., Jin, J.S.: Object tracking using CamShift algorithm and multiple quantized feature spaces, Proceedings of the Pan-Sydney area workshop on Visual information processing, pp. 3–7. Australian Computer Society, Australia (2004)

13.

Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002)CrossRef

14.

Díaz, F.J., Burón, A.M., Solana, J.M.: Haar wavelet based processor scheme for image coding with low circuit complexity. Comput. Electr. Eng. 33, 109–126 (2007)CrossRefMATH

15.

Smereka, M., Duleba, I.: Circular object detection using a modified Hough Transform. Int. J. Appl. Math. Comput. Sci. 18, 85–91 (2008)CrossRefMathSciNet

16.

Akrout, B., Mahdi, W.: Hypovigilance detection based on eyelids behaviour study. Int. J. Recent Contrib. Eng. Sci. IT 1, 39–45 (2013)CrossRef

17.

Akrout, B., Mahdi, W., Hamadou, A.B.: Drowsiness detection based on video analysis approach, 8th International Conference on Computer Vision Theory and Applications, pp. 413–416. SciTePress, Spain (2013)

18.

Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, vol. 454, pp. 903–995. (1998)

19.

Kojima, N., Kozuka, K., Nakano, T., Yamamoto, S.: Detection of consciousness degradation and concentration of a driver for friendly information service. In: Proceedings of the IEEE International Vehicle Electronics Conference, pp. 31–36. IEEE Conference Publications, Tottori (2001)

20.

Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999)CrossRefMathSciNet

21.

Bottou L., Cortes C., Denker J.S.: Comparison of classifier methods: a case study in handwritten digit recognition. In: Proceedings of the 12th International Conference on Pattern Recognition, pp. 77–82. IEEE Conference Publications, Los Alamitos (1994)

22.

Akrout, B., Mahdi, W.: A blinking measurement method for driver drowsiness detection, 8th International Conference on Computer Recognition Systems, pp. 651–660. Springer, Poland (2013)

23.

Akrout, B., Mahdi, W.: A visual based approach for drowsiness detection, IEEE Intelligent Vehicles Symposium, pp. 1324–1329. IEEE Conference Publications, Australia (2013)

24.

Akrout, B., Mahdi, W.: Vision based approach for driver drowsiness detection based on 3D head orientation, 7th International Conference on Multimedia and Ubiquitous Engineering, pp. 43–50. Springer, Korea (2013)

25.

Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: A database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3, 18–31 (2011)CrossRef

26.

MiraclHB (Multimedia InfoRmation Systems and Advanced Computing Laboratory Hypovigilance data Base). http://www.belhassen-akrout.com. Accessed 1 July 2013

27.

Senaratne, R., Jap, B., Lal, S., Hsu, A., Halgamuge, S., Fischer, P.: Comparing two video-based techniques for driver fatigue detection: classification versus optical flow approach. Mach. Vis. Appl. 22, 597–618 (2011)CrossRef

Titel: Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration
verfasst von: Belhassen Akrout
Walid Mahdi
Publikationsdatum: 01.01.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 1/2015
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-014-0644-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2015

Visual tracking based on group sparsity learning

Fast inspection for size-based analysis in aggregate processing

Tracking the articulated motion of the human body with two RGBD cameras

Hierarchical classification with reject option for live fish recognition

A robust multilevel segment description for multi-class object recognition

An image inpainting method using pLSA-based search space estimation