我們提出一個主動式的電腦視覺系統，來取得各種偵測疲勞駕駛的視覺線索，這些視覺線索包含:眼睛的開/合、眨眼、眼瞼的動作、與臉部的方向。
本系統主要由四個部份所組成：主動式的影像擷取設備、眼睛偵測器、眼睛追蹤器、和視覺線索的擷取器。為了在不同環境光源的情況下都能正確的偵測並追蹤眼睛，我們使用紅外線相機來擷取駕駛人的瞳孔與臉部的影像，並配合一個閃爍式紅外線照明器，讓影像交替的產生亮瞳與暗瞳的效果。亮瞳與暗瞳的影像有著相同的背景與外界光源，我們可以在這兩張影像相減的結果中簡單地找出瞳孔的位置，依找到的瞳孔位置剪取出眼睛的區域並利用支援向量機（Support Vector Machine, SVM） 來驗證偵測結果是否正確，如果在連續三張影像中偵測的結果都正確，則程序進入追蹤的階段。在追蹤時我們使用一個兩階段的追蹤方法，第一階段是在預測區域裡做眼睛偵測，如果第一階段失敗，則使用第二個基於比對原則的追蹤方法。
我們在不同的環境光源下評估我們的系統，像是在夜晚或黑暗中。從實驗結果中顯示，我們的方法可以在不同的環境光源下正確的偵測並追蹤眼睛的位置。

An active computer vision system is proposed to extract various visual cues for drowsiness detection of drivers. The visual cues include eye close/ open, eye blinking, eyelid movement, and face direction.
The proposed system consists of four parts: an active image acquisition equipment, eye detector, eye tracker, and visual cue extractor. For working in various ambient light conditions, we used an IR camera equipped with a blinking IR illuminator to acquire deriver’s pupils and face for detecting and tracking eyes.
The bright and dark pupil images acquired by the active equipment share the same background and external illumination; we can simply subtract the two images to extract pupils. Based on the location of a pupil, the eye region is clipped to be verified by the SVM method. If the detection is success in consecutive three frames, the procedure is turned to tracking phase. There are two stages in the tracking phase. The first-stages method is the same the detection method. If it is fail, the second tracking strategy is launched based on the matching principle.
In experiments, we conduct several experiments with various ambient light conditions, such as day and night to evaluate the proposed system. From the experimental results, we find that the proposed approach can accurately detect and track eyes in the various ambient light conditions.

[1] Bakic, V. and G. Stockman, "Real-time tracking of face features and gaze direction determination," in Proc. IEEE Workshop Applications of Computer Vision, Princeton, NJ, Oct.19-21, 1998, pp.256-257.
[2] Cortes, C. and V. Vapnik, "Support-Vector Networks," Machine Learning, vol.20, no.3, pp.273-297, 1995.
[3] D''Orazio, T., M. Leo, G. Cicirelli, and A. Distante, "An algorithm for real time eye detection in face images," in Proc. 17th Int. Conf. Pattern Recognition, Cambridge, UK, Aug. 23-26, 2004, vol.3, pp.278-281.
[4] Grace, R., V. E. Byrne, D.M. Bierman, J.-M. Legrand, D. Gricourt, B.K. Davis, J.J Staszewski, and B. Carnahan, "A drowsy driver detection system for heavy vehicles," in Proc. Conf. Digital Avionics Systems, Oct.31-Nov.7, 1998, vol.2, pp.I36/1-I36/8.
[5] Hamada, T., T. Ito, K. Adachi, T. Nakano, and S. Yamamoto, "Detecting method for drivers'' drowsiness applicable to individual features," in Proc. IEEE Int''l Conf. Intelligent Transportation Systems, Oct.12-15, 2003, vol.2, pp.1405-1410.
[6] Haro, A., M. Flickner, and I. Essa, "Detecting and tracking eyes by using their physiological properties, dynamics, and appearance," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Hilton Head Island, SC, Jun.13-15, 2000, vol.1, pp.163-168.
[7] Hayami, T., K. Matsunaga, K. Shidoji, and Y. Matsuki, "Detecting drowsiness while driving by measuring eye movement - a pilot study," in Proc. IEEE Int’l Conf. Intelligent Transportation Systems, 2002, pp.156-161.
[8] Horng, W.B., C.Y. Chen, Y. Chang, and C.H. Fan, "Driver fatigue detection based on eye tracking and dynamic template matching," in Proc. Int. Conf. Networking, Sensing and Control, Taiwan, Mar.21-23, 2004, pp.7-12.
[9] Huang, W. and M. Robert., "Face detection and precise eyes location," in Proc. 15th Int. Conf. Pattern Recognition, Barcelona, Spain, Sep. 3-7, 2000, vol.4, pp.722-727.
[10] Hutchinson, T.E., Eye Movement Detection with Improved Calibration and Speed, U.S. Patent 4950069, Apr. 1990.
[11] Ito, T., S. Mita, K. Kozuka, T. Nakano, and S. Yamamoto, "Driver blink measurement by the motion picture processing and its application to drowsiness detection," in Proc. IEEE Conf. Intelligent Transportation Systems, Singapore, Sep.3-6, 2002, pp.168-173.
[12] Ji, Q. and Y. Xiaojie, "Real-time eye, gaze, and face pose tracking for monitoring driver vigilance," Real-Time Imaging, vol.8, Issue.5, pp.357-377, Oct. 2002.
[13] Moreno, F., F. Aparicio, W. Hernandez, and J. Paez, "A low-cost real-time FPGA solution for driver drowsiness detection," in Proc. IEEE Conf. Industrial Electronics Society, Nov.2-6, 2003, vol.2, pp.1396-1401.
[14] Morimoto, C.H., D. Koons, A. Amir, and M. Flickner, "Pupil detection and tracking using multiple light sources," Image and Vision Computing, vol. 8, pp.331-335, 2000.
[15] Parikh, P., and E. Micheli-Tzanakou, "Detecting drowsiness while driving using wavelet transform," in Proc. IEEE Conf. Annual Northeast Bioengineering, Apr.17-18, 2004, pp.79-80.
[16] Popieul, J. C., P. Simon, and P. Loslever, "Using driver''s head movements evolution as a drowsiness indicator," in Proc. IEEE Intelligent Vehicles Sym., Jun.9-11, 2003, pp.616-621.
[17] Shih, S. W., Y. T. Wu, and J. Liu, "A calibration-free gaze tracking technique," in Proc. 15th Int. Conf. Pattern Recognition, Barcelona, Spain, Sep.3-7, 2000, vol.4, pp.201-204.
[18] Smith, P., M. Shah, and N. da Vitoria Lobo, "Monitoring head/eye motion for driver alertness with one camera," in Proc. Intl. Conf. Pattern Recognition, Barcelona, Spain, Sep.3-8, 2000, vol.4, pp.636-642.
[19] Smith, P., M. Shah, and N. da Vitoria Lobo, "Determining driver visual attention with one camera," in IEEE Trans. Intelligent Transportation Systems, Dec. 2003, vol.4, pp.205-218.
[20] Sobottka, K. and I. Pitas, "Extraction of facial regions and features using color and shape information," in Proc. Int. Conf. Pattern Recognition, Vienna, Austria, Aug. Aug. 25-29, 1996, pp.421-425.
[21] Stefano, L.D., and A. Bulgarelli, "A Simple and Efficient Connected Components Labeling Algorithm," in Proc. Int. Conf. Image Analysis and Processing, 1999, pp.322-327.
[22] Terrillon, J. C., M. N. Shirazi, M. Sadek, H. Fukamachi, and S. Akamatsu, "Invariant face detection with support vector machines," in Proc. 15th Int. Conf. Pattern Recognition, Barcelona, Spain, Sep.3-7, 2000, vol.4, pp.210-217.
[23] Ueno, H., M. Kaneda, and M. Tsukino, "Development of drowsiness detection system," in Proc. Conf. Vehicle Navigation and Information Systems, Yokohama, Japan, Aug.31-Sep.2, 1994, pp.15-20.
[24] Vapnik, V., The Nature of Statistical Learning Theory, Springer-Verlag, 1995.
[25] Vuckovic, A., D. Popovic, and V. Radivojevic, "Artificial neural network for detecting drowsiness from EEG recordings," in Proc. Int''l Conf. Neural Network Applications, Yugoslavia, Sep.26-28, 2002, pp.155-158.
[26] Welch, G.., and G.. Bishop, An Introduction to the Klaman Filter, 1997.
[27] Yuille, A. L., D.S. Cohen, and P. W. Hallinan, "Feature extraction from faces using deformable templates," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Diego, CA, Jun.4-8, 1989, pp.104-109.
[28] Zhu, Z., K. Fujimura, and Q. Ji, "Real-Time eye detection and tracking under various light conditions," in Proc. Eye Tracking Research & Application Sym., New Orleans, Louisiana, 2002, pp.139-144.