臺灣博碩士論文加值系統

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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