在現代化社會中，由於車輛交通意外發生頻繁，使得社會成本增加頗遽。台灣年平均事故已達六萬多件，依據統計，其中九成以上肇事因素皆人為所致。在本論文中，我們設計一個以安全監控為基礎之系統，對車內駕駛進行臉部偵測、追蹤與危險狀態分析，以便提前發現徵兆，並警示不專心、疲勞之駕駛注意突發狀況，預防意外發生。
系統主要由下列四個部分組成。第一部份為“臉部偵測”。使用改良式YCbCr色彩模組尋找膚色區域，結合平滑濾波邊緣偵測法修補缺失特徵，更用對數相減法補償區域性亮度不均問題，使臉部搜尋具有更高穩定性，比已知的膚色模組精確許多。
第二部份為“臉部追蹤”。計算相鄰畫面臉部間之關連性，校正候選目標之新邊界，並增加系統之精確性。臉部追蹤可大幅度降低時間複雜度。系統亦修正常見之部分遮蔽及後座人臉所形成的追蹤錯誤。
第三部分為“特徵分析與標記”，除了進一步提升臉部搜尋精確性，系統亦可找出眼(耳)、鼻確切位置。此外，使用有名的三角演算法計算角度，以判定臉部方向。對於不同大小之人臉、相異的姿勢與表情、亮度不均或雜訊，皆能正確處理。
第四部分為“危險分析”。藉由交通部之意外統計資料，定義五類危安事件相異權值，包含自動分析轉頭聊天、疲勞、使用手機、連續低頭及全臉遮蔽，藉由方向估計、瞳孔偵測、特徵變化及手部膚色搜尋等。
實驗由大量測試媒體影像序列得來，精確率平均達91％。且系統之FRR(False Rejection Rate)與FAR(False Acceptance Rate)皆低於10%，證明處理相異之危安事件時，我們提出之方法方法穩定且可行。

Traffic accidents occur frequently in modernized society so that the society has to pay a lot of cost. There are more than 60,000 vehicle accidents occurring each year in Taiwan. Among them, over ninety percent of accidents are caused due to the careless of drivers according to the statistical analysis.In this thesis, a surveillance-based system utilizing face detection, tracking, and hazardous status monitoring of drivers is designed. By monitoring the symptoms of non-concentration or fatigue of drivers, a warning signal can be issued in advance so as to preventing the occurring of accidents due to the lack of unawareness.
The proposed system is composed of four main parts. The first part is face detection. The modified version of YCbCr color space is adopted to obtain raw skin color images. Edge smoothing operation is employed to remedy the erroneous judgments of extracted features. Moreover, logarithmic intensity difference method is devised to compensate partial illumination. These algorithms make the task of face detection more robust and have higher accuracy than known skin-color model.
The second part is face tracking. Correlation operation is manipulated on current face and records the ones which can regulate the borders and increase the accuracy. Time complexity of the proposed method can be drastically decreased due to the performing of face tracking.
The third part is feature inspection and marking. Our proposed method can not only promote the accuracy but also mark the exact positions of features. A novel triangular-based theorem is adopted to calculate the angles of features to determine whether the considered face is frontal or profile. Moreover, it can conquer the problems of different face sizes, varying lighting conditions, varying expressions, and noises.
The forth part is the analysis of dangerous behaviors. According to statistical results conducted by the Ministry of Communications, different weighs are assigned for five hazardous events which may result in accidents. The proposed system can automatically analyze the hazardous behaviors of chatting, drowsing, phone using, consecutive head lowering, and facial occlusion by performing direction estimation, pupil detection, feature variation, etc.
Experiments were conducted on a variety of testing video sequences. An approximately 91% success rate can be achieved; besides with both false rejection rate and false acceptance rate being very low (near 10%). Experimental results reveal the feasibility and validity of our proposed system in monitoring various hazardous behaviors resulting from drivers.

[1]“道路交通事故狀況分析”, 內政部警政署,2003、2004。
[2] K.H. Rho, K.H. Park, and M.H. Han, ”Automatic mirror adjustment system
using a driver''s pupils”, Intelligent Vehicle Symposium, IEEE
vol. 1(17-21), pp 251 – 258, 2002.
[3] M. Lydon, L. Hartley, and P. Arnold, “Procedures and devices to monitor
operator alertness whilst operating machinery in open-cut coal mines”,
RRB Transport Res. Rep. RC 7433, 1999.
[4] Qiang Ji, Zhiwei Zhu, and Lan Pu, “Real-time non-intrusive monitoring and
prediction of driver fatigue”, Vehicular Technology, IEEE Transactions,
vol. 53(4), pp 1052 – 1068, 2004.
[5] Y. C. Chung, J. M. Wang, and S. W. Chen, “ Progressive Background Image
Generation”, Proceedings of 15th IPPR Conf. on Computer Vision, Graphics
and Image Processing, pp 858-865, 2002.
[6] S. L. Phung, A. Bouzerdoum, and D. Chai, “A Novel Skin Color Model in
YCbCr Color Space and Its Application to Human Face Detection” ,
Proceedings of the International Conference on Image Processing, vol. 1,
pp 289 – 292, 2002.
[7] Y. Norimatsu, S. Mita, K, Kozuka,” Detection of the Gaze Direction using
the Time-Varying Image Processing” , Intelligent Transportation Systems,
IEEE, vol. 1, pp 74-79. ,2003.
[8] K. Ashish, and R.W. Picard, “Real-Time,Fully Automatic Upper Facial
Feature Tracking”, MIT Media Laboratory, 2002.
[9] Y. Arake, N. Shimada, and Y. Shirai, “ Detection of Faces of Various
Directions in Complex Backgrounds” , Proceedings of the 16th IEEE
International Conference on Pattern Recognition, pp 409-412, 2002.
[10] Z. F. LIU, Z. S. You, and A. K. Jain, “ Face Detection And Facial
Feature Extraction in Color Image” , Proceedings of the Fifth
International Conference on Computational Intelligence and Multimedia
Applications (ICCIMA’03), pp 126-130, 2003.
[11] W. Widjojo, and K.C. Yow, “ A Color and Feature-Based Approach to Human
Face Detection” , Proceedings of the Seventh International Conference on
Control, Automation, Robotics and Vision (ICARCV’02), pp 508-513, 2002.
[12] R.T. Collins, A.J. Lipton, T. Kanade, H. Fujiyoshi, and D.Y. Tsin, ”A
System for Video Surveillance and Monitoring”, The Robotics Institute,
Carnegie Mellon University, CMU-RI-TR-00-12, 2000.
[13] J. Alan and A. Lipton, “Virtual Postman - Real-Time Interactive Virtual
Video", IASTED CGIM ''99 , 1999.
[14] M.F. Luis, and A. Sergio, “ People tracking in surveillance
applications”, Proceedings 2nd IEEE Int. Workshop on PETS , Kauai,
Hawaii, USA, 2001.
[15] M. Yasumoto, H. Hongo, and Y. Niwa,” Face direction estimation and face
recognition using multiple cameras for communication in a virtual
environment”, Industrial Electronics Society, IECON 2000. 26th Annual
Conference of the IEEE, vol. 1, pp 295-300, 2000.
[16] H. J. Lee, D.G. Sim, and R.H. Park, “Detection of facial features based
on the relaxation algorithm”, Visual, Proc. Of Communications and Image
Processing, pp 1074-1081, 2000.
[17] K.C. Yow and R. Cipolla, “Feature-based human face detection”, Image
and Vision Computing, vol. 15, pp 713-738, 1997.
[18] C. Lin and K.C. Fan, “Triangle-based approach to the detection of human
face ”, Pattern Recognition, vol. 34(6), pp 1271-1284, 2001.
[19] G. Deng, L.W. Cahill, and G.R. Tobin, “The study of logarithmic image
processing model and its application to image enhancement” , IEEE Trans.
Image Process, vol. 4(4), pp 506-512, 1995.
[20] D.S. Chen, and Z.K. Liu, “ A novel approach to detect and correct
highlighted face region in color image”, Proceedings. IEEE Conference on
Advanced Video and Signal Based Surveillance, vol. 21-22 , pp 7-12, 2003.
[21] Z. Lie, and D. Samaras, ” Face recognition under variable lighting using
harmonic image exemplars” , Computer Vision and Pattern Recognition,
Proceedings.2003, IEEE Computer Society Conference, vol. 1 (18-20), pp I-
19 - I-25, 2003.
[22] Q.Z. Wu, and B.S. Jeng, ”Background subtraction based on logarithmic
intensities” , Pattern Recognition Letters, vol. 23, pp 1529-1536, 2002.
[23] Haisong Gu, Qiang Ji, and Z. Zhu , ”Active facial tracking for fatigue
detection” , Applications of Computer Vision (WACV), Proceedings. Sixth
IEEE , vol. 3-4, pp 137-142, 2002.
[24] Y. Asfaw, B. Chen, and A. Adler, ” Impact of pose and glasses on face
detection using the red eye effect” , Electrical and Computer
Engineering, IEEE CCECE 2003. Canadian Conference, vol. 2, pp 1211-1214,
2003.
[25] T. Kurita, M. Pic, and T. Takahashi, ” Recognition and detection of
occluded faces by a neural network classifier with recursive data
reconstruction” , Proceedings. IEEE Conference on Advanced Video and
Signal Based Surveillance, vol.22, pp 53– 58, 2003.
[26] Y. Guo, G. Poulton, Jiaming Li, M. Hedley, and R.Y. Qiao, ” Soft margin
AdaBoost for face pose classification” , Acoustics, Speech, and Signal
Processing, ICASSP ''03, IEEE International Conference, vol.3, 6-10, pp
221-224, 2003.
[27] D. Chai and A. Bouzerdoum, ” A Bayesian Approach to Skin Color
Classification in YCbCr Color Space” , IEEE Region Ten Conference, Kuala
Lumpur, Malaysia, vol.2 , pp 421-424, 2000.
[28] R.M. Haralick, and LG. Shapiro, Computer and Robot Vision, vo1.4, Addison-
Wesley Inc., 1992.
[29] R.C. Gonzalez, and R.E. Woods, ”Image Segmentation”, Digital Image
Processing, vol.10 , pp 577-587, 2003.
[30] R. Jain, R. Kasturi, and B.G. Schunck, “ Machine Vision”, MaGraw-
Hill , 2005.
[31] D. Gutchess, M. Trajkovic, and D. Lyons, “ A Background Model
Initialization Algorithm for Video Surveillance”, IEEE Conference, 2001.
[32] J. Barron, D. Fleet, and S. Beauchemin, “ Performance of optical flow
techniques”, International Journal of Computer Vision, vol. 12, pp 42-
77, 1994.
[33] C. Anderson, P. Burt, and G.V. Wal, “ Change detection and tracking
using pyramid transformation techniques”, Proceedings of SPIE -
Intelligent Robots and Computer Vision, vol 579, pp 72–78, 1985.
[34] R.C. Gonzalez, and R.E. Woods , Digital image processing, Addison-Wesley
Publishing Company, Reading, MA., 1993.
[35] H.S. Lian , “ Detection of Facial Occlusions by Skin-Color based and
Local-Minimum based Feature Extractor ”, Pattern Recognition Lab.,
Computer Science and Information Engineering, NCU, 2003.
[36] “主要國家交通事故比較分析”, 交通部, 2001、2002、2003、2004。
[37] ”警政白皮書”,內政部警政署, 2001、2002、2003、2004。
[38] J. Roge, “ Variations of the level of vigilance and of behavioral
activities during simulated automobile driving ”, Accident Analysis &
Prevention, vol. 33(2), pp 181-186, 2001.
[39] 郭中仁,"以CCD影像作視向偵測", 國立清華大學電機工程所碩士 論文,1997。
[40] H.S. Gu, Qiang Ji, and Z. Zhu, ” Active facial tracking for fatigue
detection ”, Applications of Computer Vision(WACV 2002), Proceedings.
Sixth IEEE Workshop on 3-4, pp 137–142, 2002.