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研究生:黃耀慶
研究生(外文):Yao-Ching Huang
論文名稱:多攝影機監控視訊摘要以物件為主之關鍵畫面提取方法
論文名稱(外文):Summarization of Multi-view Surveillance Videos by an Object-Based Key Frame Extraction Method
指導教授:王元凱王元凱引用關係
指導教授(外文):Yuan-Kai Wang
口試委員:韓欽銓連振昌
口試委員(外文):Chin-Chuan HanCheng-Chang Lien
口試日期:2011/06/30
學位類別:碩士
校院名稱:輔仁大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:77
中文關鍵詞:視訊監控多攝影機視訊摘要關鍵畫面提取分散式系統卡爾曼濾波器
外文關鍵詞:Video surveillanceMulti-view video summarizationKey frame extractionDecentralized systemKalman filter
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視頻摘要技術在許多的研究領域是一個重要的課題,它能產生一個簡短的視訊總結給用戶用作為瀏覽和導航。因為廣大的公共安全區域會安裝多個攝影機,有龐大的非重要信息需要我們來過濾,在多攝影機視訊摘要的發展是有利於視訊監控的。在本論文中,我們提出了一個多攝影機視訊摘要的方法,從多個攝影機中提取對象語義層次的關鍵畫面。我們的目標為利用下放關鍵畫面提取方法給具主導地位的相機,避免多攝影機產生多餘的關鍵畫面。該方法藉由整合了相機選擇和關鍵畫面提取作為一種新的優化方法。
這項方法已利用大量不同監控場景的視訊以及比較其他相機選擇的方法來驗證。我们的實驗證明了這種方法不僅可以提取具代表性關鍵畫面,也減少了多攝影機多餘的關鍵畫面。
Video summarization is an important technique which has been an interested subject in many research fields which generates a short summary of a video for the presentation to users with browsing and navigation. Multi-view development is also beneficial to video surveillance, since the vast public security area installed a lot of cameras need to filter of huge non-important information. In this paper, we propose a multi-view video summarization approach that extracts semantic-level key frames by object information from multiple cameras. Our main goal is to avoid the redundant key frames with multi-view videos that the dominant camera selection presented to decentralize key frame extraction approach. The proposed approach is a new formulation which integrates camera selection algorithm into key frame extraction for optimization.
This proposed approach has been verified by large amounts video dataset that include different surveillance scenes, and comparing with other camera selection method. This method proved by experiments not only can extract representative key frames but also reduce redundant key frames in multi-view videos.

Abstract (in Chinese) i
Abstract ii
Acknowledgement (in Chinese) iii
Contents iv
List of Tables v
List of Figures vi
Chapter 1 Introduction 1
Chapter 2 Related Work 9
Chapter 3 Formulation of Decentralized Optimal Key Frame 18
Chapter 4 Local Key Frame Extraction 21
4.1 Semantic Feature Extraction 22
4.2 Feature Filtering and Fusion 23
4.2.1 Kalman Filtering 24
4.2.2 Local Feature Fusion 26
4.3 Local Key Frame Decision 29
Chapter 5 Decentralized Key Frame Extraction 32
5.1 Presentation Probabilistic Method 33
5.2 Dominant Camera Selection 34
5.3 Cluster Key Frame Decision 38
5.4 Evaluation of Camera Selection 40
5.5 Evaluation of Feature Extraction 42
Chapter 6 Experimental Results 47
6.1 Experimental Datasets 48
6.2 Evaluation of Cluster Key Frame 53
6.3 Evaluation of Decentralized Key Frame Extraction 69
Chapter 7 Conclusions 72
References 73

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