臺灣博碩士論文加值系統

近年來智慧型數位監控系統的快速發展，已不再侷限於一般傳統監控系統的功能，新式的多攝影機監控系統具備自動偵測、追蹤移動物件、辨識移動物件之身分與行為分析等功能，但功能主要著重在異常偵測與物件辨識，欠缺了攝影機彼此之間的關聯性，而且對於歷史監控畫面無法提供有效的查詢功能，因此本論文提出一套可將各個攝影機之間做關聯的環境安全監控系統，並且整合物件追蹤、辨識、物件特徵記錄與查詢功能，以滿足多攝影機監控系統的即時安全防範與危機處理需求。
本論文以電腦視覺為基礎，利用多個攝影機建置環境安全監控系統，攝影機可架設在各種場所，如針對容易發生治安死角的地點、居家與辦公大樓安全管理等。本論文以實際場景進行環境安全監控系統測試，經實驗證明，藉由本系統將各個攝影機進行關聯，可有效的記錄物件特徵、即時查閱物件行經路線，達到減少查閱歷史紀錄的時間與人力，更可提高危機處理能力。

In recent year the growth of smart digital surveillance system is boom. Except for traditional surveillance functions, a new multi-camera surveillance system has functions of automatic detection, tracing moving objects, identifying the moving object and behavior analysis. New functions focus on anomaly detection and object identification but are lack of the relations between each camera and the functions are also lack of the capability of querying about history surveillance pictures. Therefore the paper proposes an environmental surveillance system which can relate each camera and integrate functions of object tracing, object identifying, recording and querying object features. The proposed multi-camera surveillance system is able to execute real time security protection and emergency management.
The paper is based on computer vision to use several cameras to construct an environmental surveillance system. The cameras can be deployed in any kind of place, for example, to the security management of place with bad social order, to residence and to office buildings. The experiment has proved that through the connections between cameras build by the system in a real scene, the system is able to record object features sufficiently, to trace object in real time, to reduce the time for querying history records and to increase the capability of emergency management.

中文摘要 i
英文摘要 ii
目錄 iii
圖目錄 xiv
表目錄 xv
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
1.4 研究架構 3
第二章 相關研究 5
2.1 電腦視覺技術相關研究 5
2.2 環境安全監控系統相關研究 6
2.3 物件查尋與記錄技術相關研究 7
第三章 研究方法 9
3.1 系統架構 9
3.2 系統流程 10
3.3 背景建立與更新 10
3.4 物件偵測 12
3.4.1 前景影像偵測 13
3.4.2 灰階 14
3.4.3 二值化 15
3.4.4 侵蝕(Erosion)與擴張(Dilation) 15
3.4.5 相鄰元素編號演算法(Connected Component Labeling) 16
3.4.6 不感興趣物件濾除與物件記錄 17
3.5 陰影去除 19
3.6 物件辨識與追蹤 20
3.6.1 關聯性區塊比對法(Correlation Based Block Matching) 21
3.6.2 主要色彩描述演算法(Dominant Color Description) 22
3.7 多物件追蹤與辨識 24
3.8 物件特徵記錄與搜尋 27
第四章 實驗與結果 28
4.1 實驗環境 28
4.1.1 系統環境 28
4.1.2 設備規格 30
4.2 背景建立與更新 31
4.3 物件偵測 31
4.4 物件追蹤辨識 32
4.5 物件特徵記錄與搜尋 32
4.6 系統實驗成果 32
第五章 結論與未來研究方向 47
5.1 結論 47
5.2 未來研究方向 47
參考文獻 48
誌 謝 51
簡 歷 52

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