在視覺監視系統的相關研究中，移動物體的偵測一直都是重要的項目之一，而物體偵測的首要目的即是將前景物完整的從影像中分離出來。本論文針對背景相減演算法中最重要的兩個部分：背景模型的建立、光影變化的影響，以codebook模型與HSV色彩空間中陰影濾除的演算法來建立背景模型。模型中的每一個像素點有一個或多個codewords來代表背景，而每一個樣本的像素點則根據顏色與亮度的差異，將其分類到合適的codeword裡。最後，在前景偵測的階段，若新進的像素有符合的codeword，則該codeword便相應地更新；反之，則該像素點即判斷為是前景。

Motion detection is always one of the important projects in the vision surveillance related research. The purpose of the object detection is to separate the foreground object from an image accurately. This paper aims at two of the most important stages in background subtraction: background model construction and illumination variation, which build a background model based on codebook model with a shadow suppression algorithm in HSV colorspace. In the background model, each pixel can have one or more codewords representing the background. Samples at each pixel are clustered into the set of codewords based on a color distortion and brightness constraint. Finally, during the foreground detection procedure, if an incoming pixel happens to be a member of a codeword’s set, that codeword is updated accordingly; if not, the pixel is classified as foreground.

誌謝 i
中文摘要 ii
英文摘要 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1 研究動機與目的 1
1.2 論文架構 2
第二章 移動物體偵測相關文獻回顧 3
2.1 時間差異法 4
2.2 背景相減法 5
2.2.1 時間平均 6
2.2.2 高斯模型 7
2.2.3 核心密度估測 12
2.2.4 codebook模型 14
第三章 移動偵測演算法的建立 1
3.1 Codebook模型描述 17
3.1.1 初始的codebook建立 17
3.1.2 顏色和明亮度 19
3.1.3 MNRL 21
3.1.4 前景物的偵測 22
3.2 貯藏模型的建立 23
3.3 陰影偵測 24
3.4 新的Codebook演算法建立 25
3.4.1 HSV色彩空間 26
3.4.2 演算法整合 27
3.4.3 演算法架構的建立 30
第四章 實驗結果 32
4.1 實驗環境 32
4.2 實驗結果 34
4.2.1 移動的物體 35
4.2.2 非靜態的背景 37
4.2.3 陰影的偵測 39
第五章 結論及未來展望 50
參考文獻 52

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