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研究生:蘇家祥
研究生(外文):Chia-Hsiang Su
論文名稱:彩色影像中以貝氏定理為基礎之移動物件偵測
論文名稱(外文):A Bayesian Approach for Moving Object Detection in Color Videos
指導教授:王元凱王元凱引用關係
指導教授(外文):Yuan-Kai Wang
學位類別:碩士
校院名稱:輔仁大學
系所名稱:電子工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2005
畢業學年度:94
語文別:英文
論文頁數:54
外文關鍵詞:moving objectGaussian mixture modelslighting change
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移動物件的偵測是許多智慧型影像監控系統中佔有舉足輕重地位的步驟,系統後續處理諸如動作分析或移動物件之追蹤均十分仰賴偵測之結果。本論文提出一個以貝氏定理為基礎之背景相減方法達到偵測移動物件的目的,並且提出遞迴式的背景模型更新公式,得到較佳的背景影像模型,此背景影像機率模型之更新速度可隨著場景光線變化自動改變。本論文提出之方法與先前已發表之數個演算法進行模擬與實際的影像結果之比較,本方法擁有較穩定且快速的結果。
Moving objects detection is a crucial step for video surveillance. Succeeding processes such as motion object tracking and recognition highly depend on the results of detection. This paper proposes a Bayesian-based background subtraction approach for the detection of moving object. The approach devises recursive formula to better estimate the probabilistic model of background. The update rate of the probabilistic background model is adaptive to light change of environments. The proposed approach is compared to several methods in experiments of simulated and real-world videos. Fast and stable experimental results show the feasibility of our approach.
Abstract(in Chinese) i

Abstract ii

Acknowledgement(in Chinese) iii

Contents iv

List of tables vi

List of figures vii

Chapter 1 Introduction 1
1.1. Motivation 1
1.2. System overview 1
1.2.1. Background subtraction by Bayesian decision 2
1.2.2. Improved Gaussian mixture background modeling 2
1.3. Thesis organization 2
Chapter 2 Related works 5

Chapter 3 Background 14

Chapter 4 Background subtraction by Bayesian decision 21

Chapter 5 Improved Gaussian mixture background modeling 25
5.1. Parameters estimation 25
5.2. Lighting change analyzer 28
Chapter 6 Experimental results 32
6.1. Synthetic data simulations 32
6.2. Comparison of detection results 39
6.3. Image sequences tests 43
6.3.1. Introduction to image sequences 44
6.3.2. The results 45
Chapter 7 Conclusions 52

References 53
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[2]B. Shoushtarian and H. E. Bez, “A practical adaptive approach for dynamic background subtraction using an invariant colour model and object tracking,” Pattern Recognition Letters, vol. 26, no. 1, pp. 5-26, 2005.
[3]C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, 1997.
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[7]A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” in Proc. IEEE, vol. 90, no. 7, 2002, pp. 1151-1163.
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[15]D. S. Lee, “Effective Gaussian mixture learning for video background subtraction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 827-832, 2005.
[16]S. E. Chen, “QuickTime VR – An image based approach to virtual environment navigation,” in Proc. of SIGGRAPH 95, 1995, pp. 29-38.
[17]Y. Ren, C. S. Chua, and Y. K. Ho, “Statistical background modeling for non-stationary camera,” Pattern Recognition Letters, vol. 24, pp. 183-196, 2003.
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