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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 洪一平(Yi-Ping Hung) | |
dc.contributor.author | Kuan-Wen Chen | en |
dc.contributor.author | 陳冠文 | zh_TW |
dc.date.accessioned | 2021-06-13T17:29:40Z | - |
dc.date.available | 2011-07-25 | |
dc.date.copyright | 2011-07-25 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-07-12 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/39484 | - |
dc.description.abstract | 攝影機網路已廣泛應用於視訊安全監控系統中,例如:機場安全監控、車站安全監控、交通流量監控等。其主要優點在於可以監控大範圍的區域。可是隨著攝影機的數量愈來愈多,對於使用者而言,要同時觀看如此多的畫面是非常困難的。在這篇論文中,我們將探討攝影機網路安全監控系統中,於中控室監控時所需面對的主要研究課題。首先,對於跨攝影機間的事件連結,我們提出一自動學習演算法,以進行多攝影機間之目標物自動連續追蹤。於追蹤過程中的攝影機畫面切換,我們提出一主觀式平順轉場技術,以幫助使用者於攝影機切換過程中能持續監控目標物。對於大範圍與高解析度監控之應用,有別於傳統的昂貴設置方式,我們提出一多重解析度顯示設計-大小眼觀察家系統。
對於多攝影機之目標物追蹤研究,我們主要探討多攝影機之監看區域彼此間沒有重疊的情形,其困難點在於如何學習攝影機兩兩之間的時空關係與亮度轉換函式。目前該領域現有技術主要透過事先收集訓練資料並藉由人工點對應關係的方式進行學習,不過其只能應用於短時間監控或是該監控環境不會改變時。當監控環境會逐漸改變時,例如:光線變化,則這些方法會無法適應環境改變以導致追蹤錯誤。而在這篇論文中,我們提出一自動且能適應性學習的演算法,因此更能將方法應用於長時間的安全監控。 對於使用者於攝影機網路監控畫面追蹤目標物。傳統監控系統會於主要監控畫面進行直接畫面切換,可是當我們於多攝影機間進行持續追蹤,畫面會不斷切換。對於使用者而言,頻繁的直接畫面切換會造成很大的監控負擔,會很難去聯想目前使用者在環境中是從哪裡走到哪裡。因此,在這篇論文,我們提出一主觀式平順轉場技術,藉由產生攝影機間切換時的虛擬畫面,以幫助使用者更能了解當攝影機切換時的目標物移動情形。而有別於傳統視訊轉場技術,我們的方法可處理多攝影機間的監控區域是比較不同甚至不重疊的情形。 最後,我們提出一個同時具有大範圍與高解析度監控特性的多重解析度顯示系統–大小眼觀察家。該系統可同時達到高解析度顯示、高畫面更新率與低建置成本,其靈感來自於人眼視覺,只於使用者感興趣的區域顯示高解析度畫面。我們也提出一使用者測試實驗。於該實驗中,我們將所提出系統與現有方法進行比較。而實驗結果顯示,使用我們的系統,確實能有效提升使用者的監控效率。 | zh_TW |
dc.description.abstract | Camera network have been widely used in visual surveillance applications, such as airport or railway security, traffic monitoring, and etc. The main benefit of multi-camera system is that it can monitor the activities of targets over a large area. However, to security guards or users, the difficulty of monitoring such a system increases with the increase of cameras, especially when the events happen among multiple cameras. In this dissertation, we investigate two major tasks of monitoring in the command center display. One is to track targets in a camera network with computer automation. The other is to develop displaying techniques to help users to monitor the events in a camera network more easily.
First, to track targets across networked cameras, we focus on the situations where the view fields of cameras are not necessarily overlapping each other. One of the major problems of tracking across non-overlapping cameras is to learn the spatio-temporal relationship and the appearance relationship, where the appearance relationship is usually modeled as a brightness transfer function. Traditional methods learning the relationships by using either hand-labeled correspondence or batch-learning procedure are applicable when the environment remains unchanged. However, in many situations such as lighting changes, the environment varies seriously and hence traditional methods fail to work. In this dissertation, we propose an unsupervised method which learns adaptively and can be applied to long-term monitoring. Second, when monitoring the tracking activity in the camera network, the traditional surveillance systems usually switch the main camera view from one to another directly, but it makes users difficult to be aware of the trajectory of the target in the environment when switching views many times. In this dissertation, we propose a novel egocentric view transition approach, which synthesizes the virtual views during the period of switching cameras and eases the mental effort for users to understand the events. An important property of our system is that it can be applied to the situations of where the view fields of transition cameras are not close enough or even exclusive. Finally, for large-scale and high-resolution monitoring, we proposed a multi-resolution display with steerable focus, e-Fovea,. Large-scale and high-resolution monitoring systems are ideal for many visual surveillance applications. However, existing approaches have insufficient resolution and low frame rate per second, or have high complexity and cost. We take inspiration from the human visual system and propose a multi-resolution design, e-Fovea, which provides peripheral vision with a steerable fovea that is in higher resolution. In this dissertation, we further present two user studies, with a total of 36 participants, to compare e-Fovea to two existing multi-resolution visual monitoring designs. The user study results show that for visual monitoring tasks, our e-Fovea design with steerable focus is significantly faster than existing approaches and preferred by users. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T17:29:40Z (GMT). No. of bitstreams: 1 ntu-100-F93922014-1.pdf: 11410051 bytes, checksum: c643106b4cdf4d7150bb7efa90976eb0 (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | TABLE OF FIGURES xvii
TABLE OF TABLES xxiii CHAPTER 1 INTRODUCTION 1 1.1 Background and Motivation 1 1.2 Outline of this Research 3 1.2.1 Adaptive Learning for Target Tracking across Multiple Non-Overlapping Cameras 3 1.2.2 Egocentric View Transition in a Camera Network 5 1.2.3 Multi-Resolution Design for Large Scale and High-Resolution Monitoring 6 1.3 Organization of the Dissertation 6 CHAPTER 2 RELATED WORK 7 2.1 Tracking across Non-Overlapping Cameras 7 2.2 Monitoring the Tracking Activity among Multiple Cameras 10 2.3 Large-Scale and High-Resolution Monitoring System 11 2.4 Summary 13 CHAPTER 3 ADAPTIVE LEARNING FOR TARGET TRACKING ACROSS MULTIPLE NON-OVERLAPPING CAMERAS 15 3.1 Introduction 15 3.1.1 Characteristics of Our Approach 16 3.2 Problem Formulation 18 3.3 Learning Spatio-Temporal Relationship 19 3.3.1 Batch Learning Phase 20 3.3.2 Incremental Learning Phase 21 3.4 Automatic Discovering and Removing Weak Links 24 3.4.1 Remove Weak Links - Batch Learning Phase 26 3.4.2 Remove Weak Links - Incremental Learning Phase 28 3.5 Learning Brightness Transfer Function 31 3.5.1 Brightness Transfer Functions – A Review 31 3.5.2 Criterion for BTF Estimation 33 3.5.3 Spatio-Temporal Information and MCMC Sampling 34 3.5.4 Adaptively Learning BTF 35 3.5.5 Handling Sudden Illumination Change 36 3.6 Learning Fusion Weights 36 3.6.1 Basic Method 36 3.6.2 Supervised Learning Method 37 3.6.3 Unsupervised Learning Method 38 3.7 Results 39 3.7.1 Experimental Setup 39 3.7.2 Experiment on Learning Spatio-Temporal Relationship 41 3.7.3 Experiment on Learning Brightness Transfer Function 44 3.7.4 Experiment on Learning Fusion Weights 45 3.7.5 Experiment on Tracking Targets across Multiple Cameras 49 3.7.6 Discussion 51 3.6 Summary 55 CHAPTER 4 EGOCENTRIC VIEW TRANSITION IN A CAMERA NETWORK 57 4.1 Introduction 57 4.2 Motivation and Evaluation 60 4.2.1 Psychological Support 60 4.2.2 User Study: Monitoring with View Transition or Not 61 4.3 System Overview 65 4.3.1 Preprocessing 66 4.3.2 Multi-Camera Tracking 67 4.4 View Transition for Overlapping Cameras 67 4.4.1 Foreground Detection 68 4.4.2 Foreground Billboard Construction and Position Estimation 69 4.4.3 Virtual Camera Placement 69 4.5 View Transition for Non-Overlapping Cameras 70 4.5.1 Particle System 71 4.5.2 Foreground Particles Generation 71 4.5.3 Particles Movement Control 71 4.5.4 Virtual Camera Placement 72 4.5.5 Background Texture Adaptation 73 4.6 Results 74 4.5 Summary 76 CHAPTER 5 MULTI-RESOLUTION DESIGN FOR LARGE-SCALE AND HIGH-RESOLUTION MONITORING 79 5.1 Introduction 79 5.2 User Study Evaluation 81 5.2.1 Interfaces and Apparatus 82 5.2.2 User Study 1: Single Moving Target Tracking 84 5.2.3 User Study 2: Multiple Moving Target Identification 87 5.2.4 Summary 91 5.3 Discussion 92 5.3.1 No Switching and Re-orientation Required 92 5.3.2 Providing Global Context 92 5.3.3 Eliminating Clipping 93 5.3.4 Without Feeling Dizzy 93 5.4 Design and Implementation of e-Fovea 93 5.4.1 System Architecture 94 5.4.2 Camera Calibration 95 5.4.3 Projector Calibration 97 5.4.4 Projector-Camera Integration 100 5.5 Results 102 5.5.1 Evaluation of Camera Calibration 102 5.5.2 Evaluation of Projector Calibration 104 5.5.3 Demonstration 105 5.6 Summary 106 CHAPTER 6 CONCLUSION AND FUTURE WORK 109 6.1 Summary of the Dissertation 109 6.2 Future Directions 110 LIST OF REFERENCES 113 PUBLICATIONS 129 | |
dc.language.iso | en | |
dc.title | 攝影機網路之目標物追蹤與視覺化顯示 | zh_TW |
dc.title | Target Tracking and Monitoring in a Camera Network | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 李錫堅(Hsi-Jian Lee),陳祝嵩(Chu-Song Chen),傅楸善(Chiou-Shann Fuh),王聖智(Sheng-Jyh Wang),王傑智(Chieh-Chih Wang),李忠謀(Chung-Mou Lee),李明穗(Ming-Sui Lee) | |
dc.subject.keyword | 視訊追蹤,攝影機網路,視訊安全監控,無重疊區域之攝影機,時空關係,亮度轉換關係,主觀式平順轉場,影像畫面切換,中控室,多重解析度,可移動式聚焦點,異質型雙攝影機系統,使用者測試, | zh_TW |
dc.subject.keyword | Visual tracking,camera network,visual surveillance,non-overlapping cameras,spatio-temporal relationship,brightness transfer function,egocentric view transition,switching views,command center,multi-resolution,steerable focus,visual monitoring,hybrid dual-camera system,user study, | en |
dc.relation.page | 130 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-07-12 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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