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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-article-BUS8-0020-0090

Czasopismo

Fundamenta Informaticae

Tytuł artykułu

Kernelized Fuzzy Rough Sets Based Yawn Detection for Driver Fatigue Monitoring

Autorzy Du, Y.  Chen, D.  Hu, Q.  Ma, P. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Driver fatigue detection based on computer vision is considered as one of the most hopeful applications of image recognition technology. The key issue is to extract and select useful features from the driver images. In this work, we use the properties of image sequences to describe states of drivers. In addition, we introduce a kernelized fuzzy rough sets based technique to evaluate quality of candidate features and select the useful subset. Fuzzy rough sets are widely discussed in dealing with uncertainty in data analysis. We construct an algorithm for feature evaluation and selection based on fuzzy rough set model. Two classification algorithms are introduced to validate the selected features. The experimental results show the effectiveness of the proposed techniques.
Słowa kluczowe
EN fatigue detection   image recognition   fuzzy rough sets   feature selection   classification  
Wydawca IOS Press
Czasopismo Fundamenta Informaticae
Rocznik 2011
Tom Vol. 111, nr 1
Strony 65--79
Opis fizyczny Bibliogr. 24 poz., tab., wykr.
Twórcy
autor Du, Y.
autor Chen, D.
autor Hu, Q.
autor Ma, P.
Bibliografia
[1] C. C. Chiang, W. K. Tai, M. T. Yang, Y. T. Huang, C. Jaung.: A novel method for detecting lips, eyes and faces in real time, Real Time Imaging, 9(4), 2003, 277-287.
[2] D. Ciucci.:On the axioms of residuated structures: Independence, dependencies and rough approximations, Fundamenta Informaticae, 69(4), 2006, 359-387.
[3] Y. Ebisawa.: Improved video-based eye-gaze detection method, Instrumentation and Measurement, 47(2), 1998, 948-955.
[4] X. Fan, B. C. Yin, Y. F. Sun.: Yawning detection for monitoring driver fatigue, Proc. Int Conf Machine Learning Cybernetics(ICMLC), Hong Kong, China, 2, 2007, 664-668.
[5] H. Gu, Q. Ji, Z. Zhu.: Active facial tracking for fatigue detection, IEEE Workshop on Applications of Computer Vision, Orlando, Florida, 2002.
[6] M. A., Hall.: Correlation-based feature selection for discrete and numeric class machine learning, Proc. 7th International Conference on Machine Learning, 359-366.
[7] Q. Hu, D. Chen, D. Yu,W. Pedrycz.: Kernelized fuzzy rough sets, Lecture Notes in Computer Science, 5589, 304-311.
[8] Q. Hu, Z. Xie, D. Yu.: Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation, Pattern Recognition, 40, 2007, 3509-3521.
[9] Q. Ji, Z. Zhu, P. Lan.: Real time non-intrusivemonitoring and prediction of driver fatigue, IEEE Transactions on Vehicular Technology, 53(4), 2004, 1052-1068
[10] Q. Ji, X. Yang.: Real-time eye, gaze, and face pose tracking for monitoring driver vigilance, Real-time Imaging, 8, 2002, 357-377.
[11] L. M. King, H. T. Nguyen, S. K. L. Lal.: Early driver fatigue detection from electroencephalography signals using artificial neural networks, Proc. 28th IEEE EMBS Annual International Conference, New York City, USA, 2006, 2187-2190.
[12] P. Maji , S. K.Pal.: RFCM: A hybrid clustering algorithm using rough and fuzzy sets, Fundamenta Informaticae, 80(4), 2007, 475-496.
[13] Y. Matsumoto, A. Zelinsky.: An algorithm for real time stereo vision implementation of head pose and gaze direction measurement, Proc. 4th International Conference on Automatic Face and Gesture Recognition, Grenoble, France, March 2000, 499-504.
[14] T. D. Orazioa, M. Leo, C. Guaragnellab, A. Distante.: A visual approach for driver inattention detection, Pattern Recognition, 40, 2007, 2341-2355.
[15] J. R. Quinlan.: C4.5: Programs for machine learning, Morgan Kaufmann Publishers, 1993.
[16] J. R. Quinlan.: Induction of decision trees, Machine Learning, 1(1), 1986, 81-106.
[17] Y. Takei, Y. Furukawa.: Estimate of driver's fatigue through steering motion, Systems, Man and Cybernetics, Waikoloa, United States, (2), 2005, 1765-1770.
[18] Y. Tran, N. Wijesuryia, R. A. Thuraisingham.: Increase in regularity and decrease in variability seen in electroencephalography (EEG) signals from alert to fatigue during a driving simulated task, Proc. 30th Annual International IEEE EMBS Conference, Vancouver, BC, 2008, 1096-1099.
[19] R. B.Wang, L. Guo, B. L. Tong, L. S. Jin.: Monitoringmouth movement for driver fatigue or distraction with one camera, Proc. 7th International IEEE Conference on Intelligent Transportation Systems, 2004, 314-319.
[20] X. Wang, E. C. C. Tsang, S. Zhao, D. Chen, D. S. Yeung.: Learning fuzzy rules from fuzzy samples based on rough set technique, Inf. Sci., 177(20), 2007, 4493-4514.
[21] L. Yu, H. Liu.: Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research, 5, 2004, 1205-1224.
[22] S. Zhao, E. C. C. Tsang, D. Chen.: The model of fuzzy variable precision rough sets, Fuzzy Systems, 17(2), 2009, 451-467.
[23] L. Zhou, W. Z. Wu.: On generalized intuitionistic fuzzy rough approximation operators, Inf. Sci., 178(11), 2008, 2448-2465.
[24] B. Moser. On Representing and Generating Kernels by Fuzzy Equivalence Relations. Journal of Machine Learning Research, 7, 2006, 2603-2620.
Kolekcja BazTech
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