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研究生:吳岳偉
研究生(外文):Yueh-Wei Wu
論文名稱:在複雜監視場景下遺失物與遺留物之智慧型偵測
論文名稱(外文):Intelligent Detection of Missing and Unattended Objects in Complex Scene of Surveillance Videos
指導教授:郭忠義郭忠義引用關係
口試委員:薛念林張厥煒李允中
口試日期:2012-07-05
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:資訊工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:52
中文關鍵詞:背景相減法動量偵測視訊監控系統物件質心遺失物遺留物
外文關鍵詞:Missing objectUnattended objectCenter of mass (CoM)Video surveillance systemBackground subtractionMotion detection
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  • 被引用被引用:1
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
以往在重要場所都會安排保安人員,遺失物與遺留物偵測可以幫助保安人員監控重要場所的安全,而無須派駐大量的保安人員巡邏勘查。本論文發展一套在視訊監控的環境下,自動地偵測出目標物體被移出監控範圍和不明物體停留在監控範圍之MUD (Missing and Unattended object Detection)系統。研究中採用時間漸進式背景萃取出前景物件,再搭配物件質心(CoM,Center of Mass),過濾掉不可信賴的疑似遺失遺留物件,透過物件邊緣與新進影像邊緣差異度與匹配度的計算,判斷出靜態前景物件為遺失物或遺留物。MUD系統改善不規則且相似顏色的大範圍遺留物誤判,環境預設在任何複雜的環境當中,無論室內室外,人數多寡,例如:捷運出口、車站。利用MUD系統,可以準確地判斷出遺失物與遺留物,並且排除掉不合理的遺失物與遺留物。

In the past, security personnel were arranged to guard on all important occasions. Missing and unattended objects of detection could help security personnel monitor an important place of safety, and it did not require a large number of security personnel to guard these places. This study develops a MUD (Missing and Unattended object Detection) system to detect and mark the target object removed from the monitoring scene and the unknown object left in the monitoring scene. The identifying of the center of mass (CoM) was compared with the objects, thus, the unreliable pixels were filtered out. By the foreground object was compared with the current frame and the background, an object detection rule is developed to determine whether the foreground object is missing object or unattended object. MUD system can be applied to any complex environment, both indoors and outdoors, such as the subway station, where is thronged with people. Using MUD system can determine the missing and unattended object accurately and the unreasonable object is excluded in video surveillance system.

摘 要 i
ABSTRACT ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 研究貢獻 2
1.4 章節編排 3
第二章 文獻探討 3
2.1 背景相減法 5
2.2 物件質心 6
2.3 邊緣偵測 7
第三章 遺失物與遺留物偵測方法 9
3.1 遺失遺留物區域分析 9
3.1.1 影像前處理 9
3.1.2 動量遮罩 11
3.1.3 漸進式雙層背景法 13
3.1.4 前景遮罩 16
3.1.5 滯留像素與質心量測應用 17
3.2 遺失物與遺留物分類 20
3.2.1 遺失物與遺留物之分類定義 21
3.2.2 邊緣偵測 22
第四章 案例研究 27
4.1 環境描述 27
4.2 系統流程與架構 28
4.3 實驗結果 30
4.4 PETS 2006 Dataset 35
4.5 i-LIDS Dataset 38
4.6 相關研究比較 41
4.6.1 文獻[8]簡述 42
4.6.2 比較結果 43
第五章 結論與未來展望 47
5.1 結論 47
5.2 未來展望 47
參考文獻 49


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[29]i-LIDS Dataset for AVSS 2007, ftp://motinas.elec.qmul.ac.uk/pub/iLids。


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