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系統識別號 U0002-0507200701135500
中文論文名稱 根據多重參考值模型之即時動態背景切割
英文論文名稱 Real-time Dynamic Background Segmentation Based on Multiple Reference Value Model
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 95
學期 2
出版年 96
研究生中文姓名 彭建文
研究生英文姓名 Jian-Wen Peng
學號 890190035
學位類別 博士
語文別 英文
口試日期 2007-06-28
論文頁數 98頁
口試委員 指導教授-洪文斌
委員-謝文恭
委員-徐郁輝
委員-鍾興臺
委員-黃俊堯
委員-洪文斌
中文關鍵字 背景切割  參考背景模型 
英文關鍵字 Background segmentation  reference background model 
學科別分類 學科別應用科學資訊工程
中文摘要 在此論文中,對於參考背景的建立與維持提出了可靠且更準確的方法。背景的切割對於影像監視系統與相關的應用是很重要的,因為要辨識出目標﹙前景﹚之前,必須先知道在場景中哪些是前景而哪些是屬於背景。因此第一個步驟是替被偵測的場景建立一個參考背景模型,如此才能根據此參考背景擷取出前景。
在現有的文獻中,在參考背景中的每一個像素都只有一個真實背景的參考值,而在此論文中所提出的multiple reference value background model (MRV background model)中的每一個像素卻有多個參考值,因此即使是再複雜或是紊亂的場景也能被正確地建立參考背景。
背景切割中的另一個重要的步驟是背景的更新。因為被偵測的環境會一直在改變,例如移動的雲的影子或建築物的影子。因此參考背景也必須被修改以反映這些變化。否則,錯誤的前景就會因此產生。然而,這些會影響場景的因素卻很少被討論。在此論文中,全域更新與區域更新兩種策略一起被用來處理背景的更新;分別負責全部與部分的參考背景修改。因此MRV背景參考模型比其他的背景模型能正常運作更長的時間。此外,影響正確前景切割的因素也被詳細地探討。
從實驗得知,MRV背景參考模型比其他的背景模型能得到更精確的參考背景與更詳細的前景細節。除此之外,由於可靠的背景更新策略,使得系統不僅能在白天與晚上正常運作,對於攝影鏡頭與場景中背景物體的晃動也有很好的抑制能力。

英文摘要 In this thesis, we proposed a reliable and precise method for building and maintaining the reference background of a detected environment. Background segmentation (sub-traction) plays an important role in video surveillance systems and related applications. In order to extract the specific targets, applications must recognize what are objects (foreground) and what are not. Therefore, the fist step in such systems is usually to build a reference background model for the detected scene, and then the foreground can be extracted by comparing with the reference background model.
In the existing literature, each pixel of a reference background model has only one reference value to the real background of the detected scene. However, each pixel in the proposed multiple reference value (MRV) background model may have multiple reference values. Thus, even in complex or disorder scenes, reference backgrounds can also be correctly built.
Updating reference background is another important step for background segmen-tation. Because the detected scene will be changed, such as moving shadows of clouds or buildings, the reference background model must be modified to reflect these varia-tions. Otherwise, such applications will result in erroneous foreground segmentations. However, the situations of causing erroneous foreground segmentations are seldom discussed.
In this thesis, a global update and a local update methods are employed as the strategies for a reference background update; they control the entire and partial modi-fication of a reference background model, respectively. Therefore, the reference back-ground model can be used more robust than other proposed models for a long period of surveillance. In addition, the situations of causing erroneous foreground segmentations are also discussed in details.
By experimental results, the proposed method can obtain a more precise reference background model and preserves more details of a segmented foreground. Moreover, because of the reliable update strategies, the system can operate normally at daytime and nighttime. In addition, the system also can resist camera and object shaking.
論文目次 List of Figures III
List of Tables V
Chapter 1 Introduction
1.1 Motivation 1
1.2 Overview 2
1.3 Organization of the thesis 3
Chapter 2 Literature Reviews 5
2.1 Background Segmentation 5
2.2 Reference Background Models 6
2.2.1 Pure Background Model 8
2.2.2 Average Background Model 9
2.2.3 Gaussian Background Model 9
2.2.4 Mixture Gaussian Background Model 12
2.2.5 Histogram Background Model 15
2.2.6 Other Approaches 17
2.3 Update Strategies 19
2.4 Situation for Erroneous Segmentation 21
2.5 Emphases of Background Segmentation 23
Chapter 3 Preliminaries 26
3.1 Color Model 26
3.1.1 RGB Color Model 27
3.1.2 HSI Color Mode 28
3.2 Image Segmentation 29
3.3 Connected Component 31
3.4 Morphology 32
3.5 Sorting 34
Chapter 4 Background Segmentation 35
4.1 Introduction 35
4.2 Building a Reference Background 38
4.2.1 Sampling 38
4.2.2 Choosing Candidates 41
4.2.3 Deciding a Reference Background 43
4.2.4 Convergence and Non-convergence 44
4-3. Non-convergence Processing 45
4.3.1 Re-sampling 46
4.3.2 Parameters Adjusting 47
4.3.3 Oscillation Problem 49
4.4 Transferring Background and Foreground Segmentation 53
Chapter 5 Background Update 58
5.1 Introduction 58
5.2 Occasions of Update 59
5.2.1 Un-update Situations 59
5.2.2 Priority of Update 61
5.3 Global Update 62
5.4 Local Update 64
5.5 Dealing with Situations of Causing Erroneous Segmentation 68
5.5.1 Many Objects Passing 69
5.5.2 Big Objects Passing 71
5.5.3 Jam of Objects 73
5.5.4 Stopping / Leaving Objects 74
5.5.5 Slowly Moving Objects 79
5.5.6 Illumination Variations 81
Chapter 6 Experimental Results 83
6.1 Test Environment 83
6.2 Results 85
Chapter 7 Conclusions and Future Works 92
7.1 Conclusions 92
7.2 Future Works 92
Reference 95


List of Figures

1.1 Foreground segmentation 2
2.1 Flow chart of building of reference background model 5
2.2 Foreground segmentation by a pure reference background 7
2.3 Build a reference background dynamically 8
2.4 A case of the pure background model 8
2.5 The different intensity distributions 10
2.6 Foreground segmentation results by a Gaussian background model 12
2.7 A mixture Gaussian distribution approximation of intensity distribution 12
2.8 Foreground segmentation results by a mixture Gaussian background model 14
2.9 Intensity diagram 15
2.10 A case of modified histogram 16
2.11 Object contours extraction by thermal image 19
2.12 Pixel states migration 21
2.13 Discrimination between background and shadow on the RGB color model 23
3.1 RGB color model 27
3.2 HSI color model 28
3.3 Image segmentation 30
3.4 Connected components 32
3.5 N-connected 32
3.6 Opening operation 33
4.1 Flow chart of complete reference background segmentation 37
4.2 Flow char of building reference background 38
4.3 Flow chart of sampling 39
4.4 The recorded colors of pixel and each color’s count 42
4.5 Flow chart of non-convergence process 45
4.6 The homogeneous situation 49
4.7 Oscillation phenomenon 51
4.8 The part of number of recorded colors 52
4.9 The part of reference number 52
4.10 Three results of foreground segmentation of different video clips 54
4.11 Sketch of transferring a reference background 55
4.12 Three results of noise removal from three different video clips 56
4.13 Three results of opening 57
5.1 The erroneous foreground segmentation 59
5.2 The erroneous update occasion 60
5.3 The erroneous foreground segmentation by un-fixed exposure 60
5.4 Flow chart of global update 63
5.5 The foreground segmentation by unstable background model 65
5.6 The erroneous foreground segmentation by slowly moving object 65
5.7 Erroneous foreground segmentation of stop objects 66
5.8 Erosion situation of foreground 67
5.9 Flow chart of local update 68
5.10 The comparison of histograms 71
5.11 The changing of intensity histogram of big object passing 72
5.12 Un-sufficient exposure case 73
5.13 Erroneous foreground segmentation causes by static object 75
5.14 Erroneous foreground segmentation causes by leaving object 77
5.15 The static mask table 78
5.16 Flow chart of background update uses static pixel 78
5.17 Erosion of slowly moving object 79
5.18 The case of normal moving object 80
5.19 Results of dealing with slowly moving objects 80
5.20 The case of static type of illumination change 81
6.1 Complete process of building background 87
6.2 The results of objects stop and leave 87
6.3 Result of dealing with objects stop and leave 88
6.4 Result of dealing with continuous big objects passing 89
6.5 Comparisons of foreground’s completeness and noise suppression 90
6.6 Result of foreground segmentation at night 91


List of Tables

2.1 Classification table of different background models 7
2.2 Comparison table of literatures 24
5.1 Update situations 62
6.1 Descriptions of test videos 84















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