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博碩士論文 etd-0522115-123428 詳細資訊
Title page for etd-0522115-123428
論文名稱
Title
於視訊監控系統下應用威沙特矩陣特徵值分佈之偵測方法
A Detection Method using Eigenvalue Distribution of Wishart Matrices for Video Surveillance Systems
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
37
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2015-06-16
繳交日期
Date of Submission
2015-06-22
關鍵字
Keywords
廣義似然比檢測、最大似然估測、統計訊號處理、威沙特矩陣、視訊監控系統
maximum likelihood estimation, statistical signal processing, wishart matrices, generalized likelihood ratio test, video surveillance systems
統計
Statistics
本論文已被瀏覽 5694 次,被下載 45
The thesis/dissertation has been browsed 5694 times, has been downloaded 45 times.
中文摘要
近年來, 視訊監控系統已被廣泛地運用在人們的生活中, 使人們的生活環境更加舒適與安全, 但對於保全方面的監控系統通常都是作為事件發生後的證據來源, 這樣的視訊監控系統在事件發生時並無法自動地提供即時的警報。
本論文的目的是利用統計訊號處理結合數位影像資訊, 來達到監控環境內是否有異物入侵, 且此系統如果偵測到異物入侵時是能即時的發出警報並儲存該重要影像。而我們透過文獻得知影像矩陣是可以被劃分成多個區塊再各別做處理以及每個像素點分佈為高斯分佈的啟發, 利用了威沙特矩陣的特徵值分佈採用兩種方案來實現此系統, 在最後我們將利用電腦模擬分析及將此方案以照片與視訊影像來比較。
Abstract
In recent years, video surveillance systems have been widely used in our lives; it makes our environment more comfortable and secure. But the video surveillance systems are usually a source to evidence for occurrence of events. However, such video surveillance systems can’t provide real time alerts automatically when event occurs.
The purpose in this thesis is combining statistical signal processing with digital image information to monitor the environment whether foreign matters invade or not. If the system detects the foreign matters invade; it can alert and save the important images in time. We have considered the image matrix in the literature. It can be divided into several blocks and make
processing and distribution of each pixel for inspiration by Gaussian distribution respectively.
We adopt two programs by using eigenvalue distributions of Wishart matrices to implement
this system. In the end, we will simulate the programs we proposed and compare photos to
video images by using Matlab.
目次 Table of Contents
摘要 i
Abstract ii
1 序論 1
1.1 研究動機 1
1.2 研究方法 2
1.3 論文貢獻 3
1.4 論文架構 3
2 視訊監控系統之偵測方法 4
2.1 數位影像簡介 4
2.2 基於特徵值空間分解 5
2.2.1 區塊與子區塊 6
2.2.2 自相關矩陣的構成 6
2.2.3 特徵值分解 8
2.2.4 區塊特徵提取與量化及容忍區間 9
2.3 方案A 10
2.4 方案B 14
3 威沙特矩陣特徵值分佈用於視訊監控系統之模擬與驗證 17
3.1 模擬結果 17
3.2 驗證與實驗環境及架構 21
3.3 驗證方法 22
3.4 驗證結果 22
4 結論 27
參考文獻 28
參考文獻 References
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[2] 梁凱雯, “基於特徵值空間分解之影像認證系統” 國立中央大學通訊工程學系碩士論文, 2004.
[3] A. M. Tulino, S. Verdu, Random matrix theory and wireless communications, Now Publishers, 2004.
[4] R. C. Gonzalez, R. E. Woods, Digital image processing, Addison Wesley, second edition, 2007.
[5] P. A. Dighe, R. K. Mallik, and S. S. Jamuar, “Analysis of transmit–receive diversity in rayleigh fading,” IEEE Trans. Commun. , vol. 51, no. 4, pp. 694-703, 2003.
[6] A. T. James, “Distributions of matrix variates and latent roots derived from normal samples,” Ann. Math. Statist., vol. 35, pp. 475-501, 1964.
[7] S. M. Kay, Fundamentals of statistical signal processing, Volume 2: Detection theory, Prentice Hall, 1998.
[8] P. K. Varshney, Distributed detection and data fusion, Springer, 1997.
[10] 吳展維, “於未知本地感測器偵測機率下具通道知覺之分散式二元偵測” 國立交通大學電信工程學系碩士班碩士論文, 2009.
[11] G. Abreu, W. Zhang, “Extreme eigenvalue distributions of finite random wishart matrices
with application to spectrum sensing,” in Conf. Rec. 45th ASILOMAR. Conf. Signals, Syst. Comput., pp. 1731-1736, 2011.
[12] L.M. Liu, Z. Li, and E. J. Delp, ”“Efficient and low-complexity surveillance video compression
using backward-channel aware Wyner-Ziv video coding,” IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 4, pp. 453-465, 2009.
[13] Y. Rui, T. S. Huang, and S. F. Chang, “Image retrieval: Current techniques, promising directions and open issues,” J. Vis. Commun. Image Represent., vol. 10, no. 4, pp. 39-62, 1999.
[14] Z. Ji, Y. Su, R. Qian, J. Ma, “Surveillance video summarization based on moving object detection and trajectory extraction,” ICSPS, 2010 2nd International Conference on , vol. 2, pp.V2-250-V2-253, 2010.
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