簡易檢索 / 詳目顯示

回結果列表
研究生： NORMA LATIF FITRIYANI
NORMA - LATIF FITRIYANI
論文名稱： Real-Time Drowsiness Detection System Using Haar Cascade Classifier and Circular Hough Transform
Real-Time Drowsiness Detection System Using Haar Cascade Classifier and Circular Hough Transform
指導教授： 楊傳凱
Chuan-Kai Yang
口試委員: Nai-Wei Lo
Nai-Wei Lo
Bor-Shen Lin
Bor-Shen Lin
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 42
中文關鍵詞： FaceDetection 、EyeDetection 、EyeStateDetection 、HaarCascadeClassifier 、CircularHoughTransform
外文關鍵詞： Face Detection, Eye Detection, Eye State Detection, Haar Cascade Classifier, Circular Hough Transform
相關次數： 點閱：244  下載：10 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • Nowadays, technology is growing rapidly followed by modernization. Face detection technology is one technology that has been developed and applied in various sectors such as biometrics recognition systems, retrieval systems, database indexing in digital video, security systems with restricted area access control, video conferencing, and human interaction systems. From various sectors that could be developed, emerging new ideas to apply digital image face detection results further, namely eye detection. Eye detection is a further development of face detection in which the image of a human face was detected to be processed by detecting the location of both eyes on the face. Nowadays, the eye detection system can be used as a means of developing more complex applications and can be applied directly in the aspect of technology that uses eye detection like, eye state detection system, drowsiness and fatigue detection system, safety driving support systems or driver assistance system.
    In this research, a real-time eye state detection system using Haar Cascade Classifierand Circular Hough Transform (CHT) is presented. This system first detects the face and then the eyes using Haar Cascade Classifiers, which differentiate between open and closed eyes. CHT is used to detect the circular shape of the eye and also used to enhance the performance of Haar Cascade Classifier while detecting eyes. When the classifiers are not correctly classifying the eye,then the CHT will detect the circular shape in the detected eye region.
    The accuracy of face detection is 97.60% and eye detection is 98.56% for our database which contains 2856 images for open eye and 2384 images for closed eye. This system works on several stages and is fully automatic. This eye state detection system was tested by several people, and the overall accuracy of the proposed system is 96.96%.

    Abstract............................................i Acknowledgement.....................................ii List ofContent................................... ..iii List of Figure......................................v List of Table.......................................vii List of Equation....................................viii Chapter 1 Introduction..............................1 1.1Motivation..................................1 1.2Contribution................................2 1.3Thesis Organization.........................2 Chapter 2 Related Work..............................3 2.1 Haar-like Features...........................3 2.2 Cascade Classifiers..........................4 2.3 Circular Hough Transform (CHT)...............6 Chapter 3 Proposed System...........................9 3.1 System Overview..............................9 3.2 Proposed Algorithm...........................11 3.3 Face and Eye Detection.......................12 3.3.1 Features...............................12 3.3.1.1 Integral Image....................13 3.3.2 Learning Classification Functions......14 3.3.3 Creating Haar Cascade Classifier.......15 3.4 Iris Detection...............................22 3.5 Eye State Detection..........................24 3.5.1 Eye State Analysis.....................24 3.5.2 Drowsiness Analysis....................25 Chapter 4 Experimental Result.......................26 4.1 Experiment...................................26 4.2 Results......................................28 4.3 Limitation and Discussion....................36 Chapter 5 Conclusion................................37 5.1 Conclusion...................................37 5.2 Future Work..................................38 References..........................................39

    [1]Noor, H., Ibrahim, R., “A framework for measurement of humans fatigue level using 2 factors”, In: International Conference on Computer and Communication Engineering. (2008) 414–418.
    [2]Eriksson, M., Papanikotopoulos, N., “Eye-tracking for detection of driver fatigue”, In: IEEE Conference on Intelligent Transportation System (TSC). 1997. 314–319.
    [3]Thummar, S., Kalariya, V., “A real time driver fatigue system based on eye gaze detection”, in International Journal of Engineering Research and General Science, Vol. 3, 2015.
    [4]Wang, H., Zhou, L. B., Ying, Y., “A novel approach for real time eye state detection in fatigue awareness system”, IEEE Robotics Automation and Mechatronics, 2010.
    [5]Wang, Q., Yang, Z., “Eye location and eye state detection in facial images with unconstrained background”, in Journal of Information and Computing Science, Vol. 1, No. 5, 2006, pp. 284-289.
    [6]Viola, P., Jones, M., “Rapid object detection using a boosted cascade of simple features,” IEEE Computer Vision and Pattern Recognition, 2001.
    [7]New, N. S., Dr. Nyein, A., “Face and Eye Detection Using Haar Cascade Classifier and Symmetry Detection”, in International Journal of Information & Computer Science (IJITCS), Vol. 13, 2014.
    [8]Open Computer Vision Library Reference Manual. Intel Corporation, USA, 2001.
    [9] Mahdi, R., Hossein, Z. N., Sandino, M., “Global Haar-like Features: A New Extension of Classic Haar Features for Efficient Face Detection in Noisy Images”, 6th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2013.
    [10]Mahdi, R., Reinhard, K., “Adaptive Haar-like Classifier for Eye Status Detection Under Non-ideal Lighting Conditions”, Proceedings of the 27th Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 521- 526, 2012.
    [11]Mahdi, R., Reinhard, K., “Novel Adaptive Eye Detection and Tracking for Challenging Lighting Conditions”, Asian Conference on Computer Vision Workshops (ACCV), pp. 427-440, 2013.
    [12]T. M. Inc., “Train a Cascade Object Detector,” [Online]. Available: http://www.mathworks.se/help/vision/ug/train-a-cascadeobject-detector.html#btugex8. [Accessed May 2016].
    [13]Mohammadreza, R., Mohammad, A. A., Mohammad, R., “A New Method for Eye Detection in Color Images,” Journal of Advances in Computer Research, vol. 2, pp. 55-61, 2010.
    [14]Kun, P., Liming, C., Su, R., Georgy, K., “A Robust Algorithm for Eye Detection on Gray Intensity Face without Spectacles,” JCS&T, vol. 5, no. 3, October 2005.
    [15]Mahdi, R., Reinhard, K., “3D cascade of classifiers for open and closed eye detection in driver distraction monitoring”, In Proc. Computer Analysis of Images and Patterns, pp. 171-179, 2011.
    [16]Mahdi, R., Reinhard, K., “Simultaneous Analysis of Driver Behaviour and Road Condition for Driver Distraction Detection”, International Journal of Image and Data Fusion, 2, 217-236, 2011.
    [17]Gloria, C., Xiaobo, L., “Towards a system of automatic facial feature detection”, Pattern Recognition, 26,1739-1755, 1993.
    [18]Paul, V.C. Hough, “Method and means for recognizing complex patterns”, US Patent 3, 069, 654, 1962.
    [19]Azriel, R., “Picture processing by computer”, ACM Computing Surveys (CSUR) 1, no. 3, 147–176, 1969.
    [20]Richard, O. D., Peter, E. H., “Use of the hough transformation to detect lines and curves in pictures”, Communications of the ACM 15, no. 1, 1972, pp.11–15.
    [21]Marcin, S., Ignacy, D., “Circular object detection using a modified hough transform”, IJAMCS 18, no. 1, 85–91, 2008.
    [22]Marco, F., Maria, G. A., “Architectures for the hough transform: A survey”, MVA, 1996, pp. 542–551.
    [23]Saiyed, U., Bibhas, C. D., “A fast iris localization using inversion transform and restricted circular hough transform”, Advances in Pattern Recognition (ICAPR), 2015.
    [24]Muhammad, A. Z., Umer, A., Mohsin, J., Omer, G., Yasar, A., “Face and eye detection in images using skin color segmentation and circular hough transform”, Robotics and Emerging Allied Technologies in Engineering, 2014.
    [25]Nawal, A., Aouatif, A., Mohammad, R., Dris, A., “Eye state analysis using iris detection based on circular hough transform”, Media Computing and Systems, 2011.
    [26] Radu, G. B., Alexandru, P., Vlad, C., Cristian, R., Constantin, B., “Pupil center coordinates detection using the circular hough transform technique”, International Spring Seminar on Electronics Technology, 2015.
    [27] Yasutaka, I., Wataru, O., Tetshusi, W., Fumitaka, K., “Detection of eyes using circular hough transform and histogram of gradient”, Pattern Recognition, 2012.
    [28]Amer, A. R., Miad, F., “Enhance frame rate for real-time eye tracking using circular hough transform”, Systems, Applications and Technology Conference, 2013.
    [29]Mehdi, R., “Creating a Cascade of Haar-Like Classifier Step by Step”. [Online]. Availabe:https://www.cs.auckland.ac.nz/~m.rezaei/Tutorials/Creating_a_Cascade_of_Haar-Like_Classifiers_Step_by_Step.pdf. [Accessed May 2016]
    [30]Sialat, M., Khlifat, N., Bremond, F.,Hamrouni, K., “People detection in complex scene using a cascade of Boosted classifiers based on Haar-like-features”, Intelligent Vehicles Symposium, 83 – 87, 2009.
    [31]OpenCV, “Cascade Classifier Training — OpenCV 2.4.9.0 documentation,” [Online]. Available: http://docs.opencv.org/doc/user_guide/ug_traincascade.html. [Accessed May 2016].
    [32]C. Robin, “Train your own OpenCV HAAR classifier”, [Online]. Available: http://codingrobin.de/2013/07/22/trainyour-own-opencv-haarclassifier.html. [Accessed May 2016].
    [33]N. Seo, “OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features),” [Online]. Available: http://note.sonots.com/SciSoft ware/haartraining.html. [Accessed May 2016].
    [34]Marco, J. F., Jose, M. A., Arturo, D. L. E., “Real-time drowsiness detection system for an intelligent vehicle”, Intelligent Vehicle Symposium, 2008.
    [35]Noureddine, C., Fatma, Z. C., Amar, D., “Circular hough transform for iris localization”, Science and Technology, 2(5), pp.114-121, 2012.
    [36]Tucker, A. J., Johns, M. W., “The duration of eyelid movements during blinks: changes with drowsiness,” [online]. Available: http://www.mwjohns.com/wp-content/uploads/2008/09/apss_2005-06-18_the_duration_of_eyelid_movements_dur
    ing_blinks.pdf.[Accessed June 2016].

    QR CODE