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研究生:楊學雯
研究生(外文):Shyue-Wen Yang
論文名稱:影像監控系統之前景物件偵測與物件特徵萃取研究
論文名稱(外文):A Study of Foreground Object Detection and Object Feature Extraction for Image Surveillance System
指導教授:許明華許明華引用關係
學位類別:博士
校院名稱:國立雲林科技大學
系所名稱:工程科技研究所博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:104
中文關鍵詞:影像監控系統全景影像拼接標籤化與特徵萃取前景物體偵測
外文關鍵詞:labeling and feature extractionpanoramic image stitchingforeground object detectionImage surveillance system
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影像監視系統可以得到最直接的視覺監控效果。同時運用多支攝影機拍攝全面性的視覺場景也能用在許多地方,例如:銀行、交通路口。在影像監視系統中,可以分為前端的物件偵測與特徵萃取以及後端的物件分類與追蹤。本論文是針對前端部份進行探討,研究的主題包含多攝影機場景之影像拼接方法以及影像監控系統之前景物件偵測,最後運用影像標籤化技術取得每一獨立物件的特徵資訊。

在影像拼接中,我們從兩支攝影機的重疊區域中,找出色差較小的接縫線。針對靜態場景時,接縫線的路徑會避開場景中的水平邊緣,藉此消除水平對準的誤差。同時,在動態場景中,可變接縫線能繞過移動的物體,獲得鬼影消除的效果。為了檢測接縫線的品質,我們利用接縫相似度進行比較。在相同的測試影像中,我們的接縫相似度可以達到10%~23% 改善。

在前景物件偵測方面,我們以區塊式背景相減技術為基礎,將影像分成不重疊的影像區塊,並且分析每一影像區塊的紋理資訊。利用影像區塊的紋理變化,建立每一影像區塊的動態背景。首先,我們使用離散餘弦轉換係數建立背景模型。因為人眼對於低頻成份較為敏感,所以我們使用低頻的係數進行背景模型建立與前景物件偵測。利用影像區塊的低頻係數建立的動態背景,可以節省39.66% 的記憶體使用量。然而,離散餘弦轉換需要較高的計算量,我們進而提出簡單的區塊紋理轉換。在這個轉換中,我們只需要加法與減法運算,即可獲得亮度、垂直、水平與對角的四種區塊紋理。透過區塊紋理計算的方式,使我們建立動態背景的記憶體節省23.97% 。在嵌入式平台實現,每秒可以執行20張畫面,在執行速度方面獲得17.64% 改善的幅度。

在物件特徵萃取方面,利用影像標籤化技術,取得每一獨立物件的幾何特徵。我們使用單一回合標籤化方法,同時提出標籤再利用的概念,降低標籤使用量。利用這種方法,可以大幅降低記憶體的大小,同時提昇特徵萃取處理的速度。實驗結果顯示,我們的記憶體可以減少37%,而且執行速度可以達到41% 改善。
Image surveillance systems are used to perform direct visual monitoring. Moreover, multiple cameras are used to acquire complete visual coverage of locations such as banks and traffic intersections. Image surveillance systems can be divided into front-end processes for object detection and feature extraction, and back-end processes for object classification and tracking. This dissertation focuses on the front-end processes. The topics of this study are the development of a ghost-free video stitching method for multiple-camera environments, and the foreground object detection for image surveillance systems. An image labeling technique was applied to acquire feature data on foreground objects.

By using an image stitching method, we identified a variable seam line based on minimal color differences in the overlapping region of images captured by two cameras. By stitching a static scene, we located a seam line to bypass the horizontal edge, thus removing alignment artifacts. Furthermore, in dynamic scenes, the variable seam line removes moving objects to obtain a ghost-free panoramic video image. To evaluate the quality of a seam, we present a seam similarity function for evaluating color difference of a neighboring seam. In the same test sequence, our seam similarity method achieved an improvement of 10%–23%.

Regarding the foreground object detection, we used a block-based background subtraction technique. Initially, the input image is partitioned into non-overlapping image blocks. Subsequently, each image block is analyzed to obtain its textural information. According to the textural variation in each image block, we constructed a dynamic background model. First, we used discrete cosine transform (DCT) coefficients to construct the background model. Because human eyes are sensitive to low-frequency components, the low-order DCT coefficients of each block were used to construct a background model and execute foreground block detection. The block-based method can markedly reduce the required memory when constructing the background model in dynamic scenes. The experimental results showed that the proposed method uses at least 39.66% less memory than conventional method. However, calculating DCT coefficients increases the computational complexity. Therefore, we represent each block as a block texture. The block texture transform requires only addition and subtraction operations. The mean intensity and textural information (vertical, horizontal, and diagonal textures) can be obtained easily. When using block texture representation to construct the background model, the memory consumption can be reduced by 23.97%. The proposed approach was implemented using an embedded system platform. The proposed method can be applied to perform real-time operations at processing speeds of at least 20 fps, representing a 17.64% improvement.

Regarding the object feature extraction, we used an image labeling technique to obtain the feature data for each object. A single-pass labeling method was employed and a label reuse approach is presented to minimize the number of labels. This approach can efficiently reduce the memory consumption and improve the processing speed. The experimental results showed that the memory consumption is decreased by 37% and the execution time improved by 41%.
中文摘要….……………………………………………………………………………i

Abstract…………………………………………………………………………….…ii

Contents…………………………………………………………………………..…iv

List of Tables……………………………………………………………………..….vi

List of Figures…………………………………………………………………..……vii

1. Introduction…………………………………………………………………...…….1

1.1 Background and motivation…………………………………………………….1

1.2 Design objective………………………………………………………………...3

1.3 Dissertation organization ………………………………………………………4

2. Related Works……………………………………………………………………….5

2.1 Image stitching for panoramic images…………………………………………5

2.2 Foreground object detection……………………………………………………6

2.2.1 Pixel-based probability background extraction algorithm………………..8

2.2.2 Pixel-based ALW Method……………………….....…….………………10

2.2.3 Block-based background model from compressed video…..……………12

2.2.4 Block-based classifier cascade with probabilistic decision integration…13

2.2.5 Block-based major color background model……………….……………14

2.3 Image labeling and feature extraction ………………………………………16

2.3.1 Multi-pass algorithm……...………………………………………………18

2.3.2 Two-pass algorithm…….…………………………………………………18

2.3.3 Single-pass algorithm……………………………………………….……19

2.3.4 Summary…………………………………………………………….……21

3. Panoramic Image Stitching by Using a Variable Seam…………………………23

3.1 Variable seam concept…………………………………………………………23

3.2 Process flow of the variable seam line method………………………………24

3.3 Experimental results and comparison…………………………………………29

3.4 Summary…………………………….…………………………………………33

4. Block-based Background Modeling and Foreground Object Detection………….34

4.1 Foreground object detection method in the DCT domain…………………… 34

4.1.1 The DCT coefficient difference and block descriptor……………………..36

4.1.2 Block-based dynamic background model construction……………………39

4.1.3 Foreground block classification and background model updating…………39

4.1.4 Object shape refinement………………………………………………...…40

4.1.5 Experimental results and comparison……………………………………40

4.1.6 Summary…………………………………………………………………43

4.2 Foreground object detection method in block-texture representation………44

4.2.1 Block texture transformation………………………………………………45

4.2.2 Block texture background modeling………………………………………46

4.2.3 Foreground block detection and background block updating……………48

4.2.4 Boundary block detection and pixel-level refinement……………………48

4.2.5 Experimental results and comparison……………………………….……50

4.2.6 Threshold value setting and block size selection………………….……55

4.2.7 Summary……………………………….…………………………………57

5. Image Labeling and Feature Extraction…………………………………………..59

5.1 Single-pass labeling by reusing labels…………...……………………………59

5.1.1 The concept of label reuse……………………………………………….59

5.1.2 Table manipulation for label reuse………………………………………60

5.1.3 Processing flow of single-pass labeling and feature extraction…………63

5.1.4 Experimental results and comparison……………………………………65

5.2 Two-pass multi-pixel parallel labeling………………………………………66

5.2.1 Multi-pixel parallel labeling……………………………………………...66

5.2.2 MPL hardware architecture……………………………………………….73

5.2.3 MPL intellectual property design and implementation………………….77

6. Conclusion………………………………………………………………………..87

Reference…………………………………………………………………………….89
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