Abstract
A video-based driver fatigue detection system is presented. The system automatically locates the face in the first frame, and then tracks the eyes in subsequent frames. Four cues which characterises fatigue are used to determine the fatigue level. We used Support Vector Machines to estimate the percentage eye closure, which is the strongest cue. Improved results were achieved by using Support Vector Machines in comparison to Naive Bayes classifier. The performance was further improved by fusing all four cues using fuzzy rules.
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References
Lal, S.K.L., Craig, A.: Driver Fatigue: Electroencephalography and Psychological Assessment. Psychophysiology 39(3), 313–321 (2002)
Ji, Q., Zhu, Z.W., Lan, P.L.: Real-time Nonintrusive Monitoring and Prediction of Driver Fatigue. IEEE Trans. on Vehicular Technology 53(4), 1052–1068 (2004)
Smith, P., Shah, M., Lobo, N.D.: Determining Driver Visual Attention with One Camera. IEEE Trans. on Intelligent Transportation Systems 4(4), 205–218 (2003)
Bergasa, L.M., Nuevo, J., Sotelo, M.A., Barea, R., Lopez, M.E.: Real-time System for Monitoring Driver Vigilance. IEEE Trans. on Intelligent Transportation Systems 7(1), 63–77 (2006)
Hartley, L., Horberry, T., Mabbott, N., Krueger, G.P.: Review of Fatigue Detection and Prediction Technologies. National Road Transport Commision, Melbourne (2000)
Senaratne, R., Halgamuge, S.: Optimised Landmark Model Matching for Face Recognition. In: 7th International Conference on Automatic Face and Gesture Recognition, pp. 120–125 (2006)
Senaratne, R., Halgamuge, S.: Optimal Weighting of Landmarks for Face Recognition. Journal of Multimedia 1(3), 31–41 (2006)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.C.: Empirical Study of Particle Swarm Optimization. In: Proc. of the IEEE Congress on Evolutionary Computation, pp. 1945–1950 (1999)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Trans. on Evolutionary Computation 8(3), 240–255 (2004)
Stringa, L.: Eyes Detection for Face Recognition. Applied Artificial Intelligence 7(4), 365–382 (1993)
Baluja, S.: Using Labeled and Unlabeled Data for Probabilistic Modeling of Face Orientation. International Journal of Pattern Recognition and Artificial Intelligence 14(8), 1097–1107 (2000)
Cortes, C., Vapnik, V.: Support-vector Networks. Machine Learning 20(3), 273–297 (1995)
Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Popieul, J.C., Simon, P., Loslever, P.: Using Driver’s Head Movements Evolution as A Drowsiness Indicator. In: IEEE Intelligent Vehicles Symposium, pp. 616–621 (2003)
Roge, J., Pebayle, T., Muzet, A.: Variations of the Level of Vigilance and of Behavioural Activities during Simulated Automobile Driving. Accident Analysis and Prevention 33(2), 181–186 (2001)
Halgamuge, S.K.: Self-evolving Neural Networks for Rule-based Data Processing. IEEE Trans. on Signal Processing 45(11), 2766–2773 (1997)
Halgamuge, S.K., Poechmueller, W., Glesner, M.: An Alternative Approach for Generation of Membership Functions and Fuzzy Rules based on Radial and Cubic Basis Function Networks. International Journal of Approximate Reasoning 12(3-4), 279–298 (1995)
Halgamuge, S.K., Glesner, M.: Fuzzy Neural Networks - Between Functional Equivalence and Applicability. International Journal of Neural Systems 6(2), 185–196 (1995)
Halgamuge, S.K.: A Trainable Transparent Universal Approximator for Defuzzification in Mamdani-type Neuro-fuzzy Controllers. IEEE Trans. on Fuzzy Systems 6(2), 304–314 (1998)
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Senaratne, R., Hardy, D., Vanderaa, B., Halgamuge, S. (2007). Driver Fatigue Detection by Fusing Multiple Cues. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_96
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DOI: https://doi.org/10.1007/978-3-540-72393-6_96
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