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研究生:游炳賢
研究生(外文):Ping-Hsien You
論文名稱:以編碼簿模型結合紋理特徵之背景濾除
論文名稱(外文):Background Subtraction Using Texture Feature and Codebook Model
指導教授:王圳木王圳木引用關係林耿呈
指導教授(外文):Chuin-Mu WangGeng-Cheng Lin
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:80
中文關鍵詞:背景濾除前景偵測視覺監控編碼簿模型局部二元模式
外文關鍵詞:Background SubtractionForeground DetectionVisual SurveillanceCodebook ModelLocal Binary Pattern
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  • 被引用被引用:0
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隨著科技的發達,監控系統已經廣泛的應用在日常生活中。而監控系統中,背景濾除是相當重要的技術。然而,在週遭環境中存在著不同的動態背景,對於背景濾除是一個具有挑戰性的問題,例如:樹葉的晃動、水面的波動、顯示器的閃爍、光影變化,以上情況都容易造成背景濾除錯誤的分析。
本論文提出了以編碼簿模型結合紋理特徵的背景濾除演算法,利用編碼簿模型來建立背景模型,編碼簿模型的好處在於能夠有效的壓縮資訊,來達到最佳的處理速度。利用局部二元模式(Local Binary Pattern;LBP)取得紋理特徵,由於LBP對於紋理的敘述能力相當強,而且在計算上相當快速。接著,將經過連通物件法的紋理圖與編碼簿模型的結果做結合,能有效的提升精確度,並且降低錯誤率(False Positive rate;FP)。另外,為了更適應目前環境,加入了短期資訊模型來改善背景模型的更新。並且針對全域光線變化,加入了基於梯度的時間差異法來解決。
實驗結果顯示,我們提出的演算法能夠即時處理並且具有較佳的適應性,在不同的環境下測試,都能得到不錯的辨識率。

Due to the fast development of computer, surveillance systems are widely applied in our daily life. In surveillance systems, how to get a robust background subtraction algorithm is one of important issues. However, there are several dynamic backgrounds in the surrounding environment. Background subtraction has many challenge problems needed to solve, such as waving tree, ripple water, flashing screen, light change, etc. In those case mentioned above are easy to cause the error analysis of background subtraction.
This paper proposes codebook model combine texture for background subtraction. We construct the background model to obtain the foreground by using codebook model, which is mainly used to compress information to achieve high efficient processing speed. We use a method of local binary pattern to obtain texture features. Due to local binary pattern is very strong for the texture of the narrative ability, and in the calculation is quite fast. We combine the connected-component labeling method of texture and the result of codebook model, which can enhance the precision and reduce the false positive. Moreover, in order to adapt to the current environment, the short term information is employed to improve background updating. We add the gradient-based temporal differencing to overcome the problem of global light change.
Experimental results using different types of video sequences are presented to demonstrate the robustness, accuracy, and time responses of the proposed method.

目錄
中文摘要 ii
ABSTRACT iv
誌謝 vi
表目錄 x
圖目錄 xii
第一章、緒論 1
1.1 研究動機 1
1.2 系統流程 3
1.3 論文架構 4
第二章、相關文獻與探討 5
2.1 背景濾除的相關問題 5
2.2 移動物件偵測方法 8
2.3 背景濾除演算法 11
2.3.1 高斯混合模型(Mixture of Gaussian) 12
2.3.2 編碼簿模型(Codebook Model) 19
2.3.3 局部二元模式(Local Binary Pattern) 20
第三章、以編碼簿模型結合紋理特徵之背景濾除 24
3.1 前言 26
3.2 簡介 27
3.3 背景模型 29
3.3.1 背景模型建立 30
3.3.2 比對函式 33
3.3.3 前景偵測階段 37
3.3.4 短期資訊模型 39
3.4 基於紋理之連通物件法 42
3.4.1 局部二元模式(Local Binary Pattern) 42
3.4.2 連通物件法(Connected–Component Labeling) 44
3.5 背景濾除階段 46
3.6 基於梯度之時間差異法 47
3.7 形態學(Morphology) 50
第四章、實驗結果 53
4.1 測量方式 53
4.2 引導錯誤 60
4.3 偽裝 62
4.4 光線緩慢變化 64
4.5 光線急遽變化 66
4.6 動態背景 68
4.7 移動物體 70
4.8 前景隙縫 72
4.9 執行速度 74
第五章、結論 75
參考文獻 76

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