交通事故死亡率在國內死亡率排名總是居高不下，其中肇事的主因多來自於駕駛者精神狀態不好所造成，因此有一部分的視覺式駕駛監控系統中，嘗試利用攝影機拍攝駕駛者的臉部狀態，利用臉部特徵的擷取，來進行其精神狀態的分析。為了能使這樣的系統在不同光照環境中也能穩定的運作，本論文主要在研究視覺式駕駛監控系統中，以影像補償的方式使影像回覆影像原始色彩，使系統在不受光源的影響下亦可快速即時的進行人臉定位。
本研究使用一般攝影機進行拍攝，本研究首先會對攝影機拍攝的影像序列選取參考影像，其目的是為了提供給稍後影像補償的動作使用，在參考影像的選取上，利用了五種特徵：邊緣的空間分佈(Compactness of Spatial Distribution of Edges)、色調統計(Hue Count)、膚色統計(Skin Color)、對比度(Contrast)和模糊程度(Blur)，來進行參考影像的選取。接著對影像序列進行分鏡偵測，藉由兩張前後相鄰影像間相關的趨勢，判斷場景是否發生變化，若產生變化。則進入的影像將利用K-L transform的方式將需補償影像之色彩分佈，轉換至參考影像的色彩分佈。最後利用Adaboost的方式進行人臉偵測和以粒子群最佳化為基礎的粒子濾波器(Particle swarm optimization- based particle filter)進行追蹤，並將偵測和追蹤結合，以追蹤輔助偵測、偵測確認追蹤的方式來輸出人臉定位的結果。

Facial expressions convey rich inward feelings, including both psychological (e.g., cheer, anger, delight, frustration, disgust, fear, and surprise) and physiological (e.g., vitality, fatigue, drowsiness, attention, and distraction) reactions. Humans can easily identify the inward reactions based on facial expressions. A system that can sense the inward feelings of a driver will be of great help for driving safety. To this end, the driver’s face should first be located. In this paper, we focus on a vision-based detection and tracking of the driver’s face in the input video sequence while driving.
The major difficulty with the above task is illumination variations resulting from sunshine, shadows, environmental lights, underground passages, overheads, and tunnels. To deal with this difficulty, we develop a process that consists of three steps: reference image selection, illumination variation detection, and lighting compensation. The process keeps eye on the input video sequence in order to maintain to some extent its image quality. The driver’s face is then detected using the Adaboost technique and is tracked using the particle swarm optimization method applied to the resultant video sequence. The proposed technique was shown to work well in a number of experimental video sequences with different conditions of illumination, driver, gender, and wearing. A high face location rate around 98% has been achieved.

[Nea06] Vicki L. Neale, Thomas A. Dingus, Sheila G. Klauer, Jeremy Sudweeks, “AN OVERVIEW OF THE 100-CAR NATURALISTIC STUDY AND FINDINGS.” National Highway Traffic Safety Administration, United States Paper Number 05-0400, 2006.
[Can02] J. L. Cantero, M. Atienza and R. M. Salas, “Human Alpha Oscillations in Wakefulness, Drowsiness Period, and REM Sleep: Different Electroencephalographic Phenomena within the Alpha Band,” Neurophysiol Clin 32:54–71, 2002.
[Gaf03] Philipp P. Caffier, Udo Erdmann and Peter Ullsperger, ”Experimental evaluation of eye-blink parameters as a drowsiness measure,” EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY Volume 89, Numbers 3-4, 319-325. 2003.
[Uen94] H. Ueno, M. Kaneda and M. Tsukino, “Development of Drowsiness Detection System,” Proc. of Conf. on Vehicle Navigation and Information Systems, pp. 15 -20, Aug. 1994.
[Smi03]Smith, P.; Shah, M.; da Vitoria Lobo, N., “Determining driver visual attention with one camera,” Intelligent Transportation Systems, IEEE Transactions on. 2003.

[Ji04] Q. Ji, Z. Zhu, P. Lan, “Real-time Nonintrusive Monitoring and Prediction of Driver Fatigue,” Trans. of IEEE on Vehicular Technology, vol. 53, no. 4, pp. 1052 -1068, Jul. 2004.
[Hor04] W. B. Horng, C. Y. Chen , Y. Chang and C. Hai Fan, “Driver Fatigue Detection Based on Eye Tracking and Dynamic, Template Matching,” Proc. of IEEE Int’l Conf. on Networking, Sensing and Control, vol. 1, pp. 7 -12, Mar. 2004.
[Bor96] J. S. Boreczky and L. A. Rowe, “Comparison of Video Shot Boundary Detection Techniques.” In Storage and Retrieval for Still Image and Video Databases IV, Proc. SPIE 2664, pp. 170-179, Jan. 1996.
[Zab95] R. Zabih, J. Miller, and K. Mai, “A Feature-Based Algorithm for Detecting and Classifying Scene Breaks. Proc. “ ACM Multimedia 95, San Francisco, CA, pp. 189-200, Nov. 1995.
[Can86] J. Canny, “A Computational Approach to Edge Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 34-43, Nov. 1986.
[Lie99] Rainer Lienhart, “Comparison of Automatic Shot Boundary Detection Algorithms,“ Microcomputer Research Labs, Intel Corporation, Santa Clara, CA 95052-8819, Rainer.Lienhart@intel.com ,1999.

[Tra92] Trahanias, P.E. , Venetsanopoulos, A.N., “Color image enhancement through 3-D histogram equalization,” Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on, 1992
[Ben05] Eric P. Bennett , Leonard McMillan ,”Video enhancement using per-pixel virtual exposures,” ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2005TOG Homepage archive Volume 24 Issue 3, July 2005 ACM New York, NY, USA
[Tom98] Carlo Tomasi, Roberto Manduchi, “Bilateral Filtering for Gray and Color Images,” proceeding of the ICCV 1998
[Lan71] Edwin H. Land, John J. McCANN, “Lightness and Retinex Theory.” Journal OF THE OPTICAL SOCIETY OF AMERICA, Vol. 61. No. 1, 1-11, January 1971.
[Rah96] Rahman, Z., Jobson,D.J., Woodell, G.A.,” Multi-scale retinex for color image enhancement,” Image Processing, 1996. Proceedings., International Conference on
[Job02] Jobson,D.J., Rahman, Z., Woodell,G.A., “A multiscale retinex for bridging the gap between color images and the human observation of scenes,” Image Processing, IEEE Transactions on, 2002.

[Sha07] Feng Shao, Gang-yi Jiang, Mei Yu, Ken Chen, ” A CONTENT-ADAPTIVE MULTI-VIEW VIDEO COLOR CORRECTION ALGORITHM,” Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on.
[Sha10] Feng Shao, Gang-Yi Jiang, Mei Yu, Yo-Sung Ho, “Fast color correction for multi-view video by modeling spatio-temporal variation,” Journal of Visual Communication and Image Representation Volume 21, Issues 5-6, July-August 2010, Pages 392-403 Special issue on Multi-camera Imaging, Coding and Innovative Display.
[Zuz06] Zuzana Cˇ erneková, Ioannis Pitas, Senior Member, IEEE, and Christophoros Nikou, Member, IEEE, “Information theory-based shot cut/fade detection and video summarization,” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 16, NO. 1, JANUARY 2006.
[Ke06] Yan Ke, Xiaoou Tang, Feng Jing, “The Design of High-Level Features for Photo Quality Assessment,” Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’06), 2006.
[Vio04] Paul Viola and Michael J. Jones, "Robust Real-Time Face Detection
, " International Journal of Computer Vision 57(2), 137–154, 2004.
[Lee04] D. Lee, “Online adaptive Gaussian mixture learning for video applications, ” Workshop on Statistical Methods for Video Processing, pp. 105-116, 2004.
[Mor03] V. Morellas, I. Pavlidis and P. Tsiamyrtzis, “Deter: detection of events for threat evaluation and recognition,” Machine Vision and Applications, pp. 29-45, 2003.
[1] L. Frost. The A-Z of Creative Photography. Amphoto Books,1998
[2] OpenCV為Intel®開源電腦視覺庫。它由一系列 C 函數和少量 C++ 類構成，實現了圖像處理和電腦視覺方面的很多通用演算法。
[Xu08]Juanfang Xu; Shouqian Sun; Yuanwu Shi; Zhanxun Dong, , “Implementation of Digital Chime-bell Interaction System Driven by Hand Gesture,” Pervasive Computing and Applications, 2008. ICPCA 2008. Third International Conference on, 6-8 Oct. 2008
[3]G.Wyszechi and W.S.Stiles, Color Science:Concepts and Methods.Quantitative Data and Formulae,2nd ed.New Tork: Wiley,1982.
[4]Rastislav Lukac and Konstantinos N. Plataniotis, Color image processing.2nd edition, 2007.
[5]D.B.Judd and G.Wyszechi, Color in Business, Science, and Industry,3rd ed. New York: Wiley, 1975.
[Lie02]Lienhart, R. and Maydt, J., "An extended set of Haar-like features for rapid object detection", ICIP02, pp. I: 900-903, 2002
[Zhe08]Yuhua Zheng and Yan Meng, ”Swarming particles with multi feature model for free-selected object tracking” ,Intelligent Robots and Systems,2008.
[Ebe01]Russell C. Eberhart and Yuhui Shi,”Tracking and Optimizing Dynamic Systems with Particle Swarms”,Evolutionary Computation,2001.Proceeding of the 2001 Congress on.
[Kob07]Kobayashi,T., Nakagawa,K. ,Imae,J. and Guisheng Zhai, “Real time object tracking on video image sequence using particle swarm optimization”,Control,Automation and Systems,2007,International Conference on.
[Isa96]Michael Isard and Andrew Blake, “Contour tracking by stochastic propagation of conditional density,” Proc. European Conference on Computer Vision, vol. 1, pp.343-456,1996.
[Cho06]Jung-Uk Cho, Seung-Hun Jin, Xuan-Dai Pham, Jae-Wook Jeon, Jong-Eun Byun and Hoon Kang,”A Real-Time Object Tracking System Using a Particle Filter” IEEE/RSJ International Conference on Intelligent Robots and Systems pp.2822-2827,2006.
[Ebe00] Russell C. Eberhart and Yuhui Shi,”Comparing inertia weights and constriction factors in particle swarm optimization.” Proceeding of the 2000 Congress of Evolutionary Computation , Vol , 84-88, California, CA, USA, IEEE, Piscataway, NJ, USA.
[Pin05]Z.Li-Ping, Y.Huan-Jun, and H.Shang-Xu,”Optimal choice of parameters for particle swarm optimization”, Journal of Zhejiang University: Science, Vol 6 A, 528-534,Zhejiang University,Hangzhou,310027,China,2005.
[Yan02]Ming-Hsuan Yang, Member, IEEE, David J. Kriegman, Senior Member, IEEE, and
Narendra Ahuja, Fellow, IEEE,” Detecting Faces in Images: A Survey”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 24, NO. 1, JANUARY 2002
[Sob96]K.Sobottka and I.Pitas,”Face Localization and Feature Extraction Based on Shape and Color Information”, Proc. IEEE Int’l Conf. Image Processing,pp.483-486,1996
[Hsu02] R. L. Hsu, A. M. Mottaleb and A. K. Jain, “Face Detection in Color Images,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696 -706, May 2002.
[Kim00]S.H.Kim and H.G. Kim,”Face Detection using Multi-model Information,” Proc. Fourth IEEE International Conference Automatic Face and Gesture Recognition,pp.14-19,2000
[Mas01]Masoud, O.,Papanikolopoulos, and N.P.”A Novel Method for Tracking and Counting Pedestrians in real-time using a Single Camera.” IEEE Transactions on Vehicular Technology,vol.50, no.5,pp.1267-1278,2001.