物件偵測演算法是應用於智慧型影像系統中的基礎研究，尤其大量被用於智慧型監控系統與智慧型傳輸系統上，目的在於透過有效的偵測物件，使其資訊能夠提供給物件追蹤或辨識等相關應用，提升系統之效能，一般而言，大部分的研究常使用背景相減法，利用畫面之間的差異來擷取物件，而這樣的方式通常需要訂定門檻值將影像中的資訊分類，可是在傳統的作法中，此門檻值通常為定值，所以常需要因為測試環境的不同而重新定義，此外，陰影成份也是一項影響物件偵測演算法準確率的因素之ㄧ，所以在將影像資訊分類後，必須再透過陰影去除相關之演算法，將陰影成份去除以取出正確的物件資訊，所以在本論文中，我們提出一個根據陰影成份的特性分析，以像素點為基礎，個別定義其適應性之門檻值應用於物件偵測之演算法，使物件切割與陰影去除的動作，只要透過一個步驟的強化切割演算法，便能夠即時處理並準確的偵測物件，並且能夠將陰影成份的干擾降到最低，使偵測出來的物件資訊能夠更加的與實際物件吻合，實驗結果顯示，即使是在室內、戶外或是雨天的環境下，透過我們所提出的方法也都能夠快速並有效的偵測物件。

Object detection Algorithm is a fundamental research for intelligent video system. It is widely used in intelligent surveillance system and Intelligent Transportation System (ITS). The purpose is to improve the performance of the system for object tracking or object recognition by detecting object effectively. In general, most of the research is using the difference between the frames to detect objects by background subtraction method. But it needs to set a fixed threshold to classify objects and background in traditional methods. It must redefine the threshold value when the testing environments are changed. Furthermore, shadow component is also a factor to interfere with the correctness of object detection algorithm. It needs to use a shadow removal algorithm to refine the parts of object after classifying objects and background.
In this thesis, we present an enhanced segmentation algorithm with a pixel-dependent threshold for locating shadow regions according to the characteristic of shadow to reduce the interference of shadows and segment objects effectively in real-time. With that, the shadow regions are located more accurately and the moving objects are extracted more completely. Experimental results verify the proposed approach and show that it is helpful for object detection in various environments.

[1] H. Qian, X. Wu and Y. Xu, Intelligent Surveillance Systems. Springer, 2011. Available at
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-94-007-1136-5.
[2] S. Tsugawa, T. Saito and A. Hosak, “Super Smart Vehicle System: AVCS Related Systems for The Future,” in Proc. of Intelligent Vehicles '92 Symposium., Detroit, MI, USA, Jun. 1992, pp.132–137.
[3] M. Padmadas, K. Nallaperumal, V. Mualidharan and P. Ravikumar, “A deployable architecture of Intelligent Transportation System－A developing country perspective,” in Proc. IEEE Computational Intelligence and Computing Research (ICCIC), India, Dec. 2010, pp.1–6.
[4] 交通部運輸研究所, Available at http://www.iot.gov.tw/ct.asp?xItem=250407&CtNode=2381&mp=3.
[5] 內政部警政署, Available at http://www.npa.gov.tw/NPAGip/wSite/lp?ctNode=11393&CtUnit=1739&BaseDSD=7&mp=1.
[6] 經濟部投資業務處, “智慧型車輛產業分析及投資機會,” 97年2月, Available at http://investtaiwan.nat.gov.tw/doc/industry/07Intelligent_Vehicles_cht.pdf.
[7] 公路總局國道替代道路監視系統, Available at http://61.60.46.205/mtc/INDEXS/index2thb2/index2.htm.
[8] 國道高速公路交通資訊系統–路況圖, Available at http://1968.freeway.gov.tw.
[9] Lighting Design and Simulation Knowledgebase, Available at http://www.schorsch.com/en/kbase/glossary/penumbra.html.
[10] What is an Eclipse, Available at http://www.mcglaun.com/eclwhatis.htm.
[11] J. Stauder, R. Mech and J. Ostermann. “Detection of Moving Cast Shadows for Object Segmentation,” IEEE Trans. Multimedia, vol.1, issue.1, pp.65–76, Mar. 1999.
[12] M. Piccardi, “Background subtraction techniques: a review,” in Proc. IEEE Int. Conf. Systems, Man, Cybernetics, Apr. 2004, pp. 3099–3104.
[13] R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process., vol. 14, no. 3, pp. 294–307, Mar. 2005.
[14] R. Girisha and S. Murali, “Segmentation of Motion Objects from Surveillance Video Sequences using Partial Correlation,” in Proc. IEEE Int. Conf. on Image Processing (ICIP), Cairo, Egypt, Nov. 2009, pp. 1129–1132.
[15] L. Maddalena and A. Petrosino, “Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications,” IEEE trans. Image Processing, vol. 17, no 7, pp. 1168–1177, Jul. 2008.
[16] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd edition, New Jersey, Prentice Hall, 2002.
[17] C. Stauffer and W.E.L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Fort Collins, CO, USA, Jun. 1999, pp. 246–252.
[18] C. Stauffer and W.E.L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp.747–757, Aug. 2000.
[19] C. B Do and S. Batzoglou, “What is the expectation maximization algorithm,” npg Trans. Nature Biotechnology, vol. 26, no. 8, pp.897–899, Aug. 2008.
[20] S. Zhang, H. Yao, and S. Liu, “Dynamic Background Modeling and Subtraction Using Spatio-Temporal Local Binary Patterns,” in Proc. IEEE Int. Conf. on Image Processing (ICIP), San Diego, California, Oct. USA, 2008, pp. 1556–1559.
[21] R. P. Avery, G. Zhang, Y. Wang, and N. L. Nihan. “An Investigation into Shadow Removal from Traffic Images,” Transportation Research Record: Journal of the Transportation Research Board (TRB), Vol. 2000, pp. 70–77, Nov. 2007.
[22] A. Bevilacqua, “Effective Shadow Detection in Traffic Monitoring Applications,” Journal of WSCG, Vol. 11, No. 1, pp 57–64, Feb. 2003.
[23] A. W. K. So, K. K. Wong, R. H. Y. Chung, and F. Y. L. Chin, “Shadow detection for vehicles by locating the object-shadow boundary,” in Proc. SIP conf., Paris, Jun. 2005, pp.315–319.
[24] J. C. Lai, S. S. Huang, and C. C. Tseng, “Image-Based Vehicle Tracking and Classification on the Highway,” in Proc. IEEE Int. Conf. on Green Circuits and Systems (ICGCS), Shanghai, China, Jun. 2010, pp. 666–670.
[25] C. C. Chiu, M. Y. Ku, and L. W. Liang, “A Robust Object Segmentation System Using a Probability-Based Background Extraction Algorithm,” IEEE trans. Circuits Syst. Video Technol., vol. 20, no. 4, pp. 518–528, Apr. 2010.
[26] 古閔宇，智慧型影像監控與辨識系統，博士論文，國防大學理工學院國防科學研究所，民國九十八年。
[27] 陳登昌，基於電腦視覺之即時三維人體姿態特徵點估測，碩士論文，國立中興大學電機工程學系，民國九十八年。
[28] S. Y. Chien, S. Y. Ma, and L. G. Chen, “Efficient Moving Object Segmentation Algorithm Using Background Registration Technique,” IEEE trans. Circuits Syst. Video Technol., vol. 12, no. 7, pp. 577–586, Aug. 2002.