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系統識別號 U0002-1707200716072500
中文論文名稱 靜物異狀偵測系統
英文論文名稱 A Video Surveillance System for Detecting Abnormal Events Arisen from Static Foreground Objects
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 95
學期 2
出版年 96
研究生中文姓名 陳威州
研究生英文姓名 Wei-Chou Chen
學號 692191215
學位類別 碩士
語文別 中文
口試日期 2007-06-29
論文頁數 65頁
口試委員 指導教授-洪文斌
委員-謝文恭
委員-徐郁輝
委員-洪文斌
中文關鍵字 智慧型系統  視訊監控  滯留物偵測 
英文關鍵字 surveillance systems  video surveillance  abandoned object  abnormal events 
學科別分類 學科別應用科學資訊工程
中文摘要   在本論文中,我們提出一個能夠自動偵測監控場景中由靜態前景物所引起的異常事件(靜物異狀)的視訊監控系統。當偵測到靜物異狀時,此系統除了會發出警示訊號提醒監控人員,並可提供事件發生時此系統所擷取的關鍵影像畫面,做為判讀該異常事件的輔助資訊。
  本系統所使用的靜物異狀偵測方法,分為三個階段:首先,利用統計式背景建構法和滑動暫存區(Sliding Buffer)概念結合而成的背景建立與更新機制,搭配一個雙層背景架構,分別產生參考背景和瞬時背景之後,再將兩張背景影像相減,便得到可能包含靜態前景物的差異影像;其次,將上一階段獲得的差異影像經過灰階轉換、自動門檻值法、侵蝕和擴張運算、連通元件標示等一連串處理後,擷取出差異影像中每一個連通元件的面積與位置資訊;最後,根據每個連通元件的面積資訊,判斷監控場景中是否出現靜態前景物,若是,則發出偵測到靜物異狀的警示訊息。
  本系統在Pentium-M 1.60GHz的CPU與1GB的RAM,輸入影像為320 x 240 RGB全彩 (24 bit) Bitmap環境下,平均一秒鐘能夠處理10畫格;系統正確率則在85%以上。與其他偵測遺留物的方法比較,本論文提出的方法在原理上相對簡單、改進了處理效率、並能夠準確地在指定的停滯時間到達時偵測到靜態前景物。
英文摘要   In this paper, we describe a video surveillance system that automatically detects abnormal events arisen from static foreground objects, which includes both abandoned and removed ones in the monitored scene. While a abnormal event arisen from static foreground objects is detected, the operator is given notice to take care of this event, and the system provides appropriate key frames for interpreting this abnormal event.
  The method of our proposed system consists of three major phases. First, we utilize a scheme of background initialization and updating, which incorporates a background model of histogram estimation and the concept named “Sliding Buffer”, to be the foundation of the two-layer background model. This two-layer background model is used to generate current background image and reference background image. The difference image that might contain static foreground objects is then obtained by subtracting current background image and reference background image. Second, the system applies a sequence of image processing techniques, including gray-level transformation, Otsu automatic thresholding method, morphological erosion and dilation operations and connected component labeling, on the difference image acquired in previous phase to obtain the area and position information of each connected component. Finally, the system determines if a static foreground object exists in the monitored scene according to area information of each connected component. If a static foreground object is detected, the system issues an alarm of abnormal event.
  Our system is tested under Pentium-M 1.60GHz CPU and 1GB RAM; the format of input image is 320 x 240 true color (24 bits) Bitmap. The performance of our system can reach 10 frames per second (about 0.09 ~ 0.18 seconds to process a frame according to different environment), and the average accuracy of system is higher than 85%. Comparing with other detection methods, our proposed method is relative simpler in theorem, improves the efficiency, and is capable of detecting static foreground object precisely in time while its stagnant time reaches a given threshold.
論文目次 目錄 I
圖目錄 IV
表目錄 VI
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 5
1.3 論文架構 5
第二章 文獻探討 7
2.1 基本影像處理介紹 7
2.1.1 直方圖 6
2.1.2 灰階轉換 8
2.1.3 自動門檻值計算:Otsu法 9
2.1.4 型態學:侵蝕與擴張運算 11
2.1.5 連通元件標示 13
2.2 相關研究回顧 14
2.2.1 Spengler-Schiele的著作 14
2.2.2 陳又慈的著作 14
2.2.3 祝珮軒的著作 15
2.2.4 其他著作 19
第三章  靜物異狀偵測系統 20
3.1 系統架構 23
3.2 背景模組 27
3.2.1 瞬時背景 32
3.2.2 參考背景 36
3.2.3 差異影像 36
3.3 影像處理模組 36
3.3.1 灰階轉換 37
3.3.2 影像二值化 40
3.3.3 型態學:侵蝕和擴張運算 41
3.3.4 連通元件標示 42
3.4 物體偵測模組 43
第四章  實驗結果 44
4.1 實驗環境 44
4.2 程式介面 45
4.3 實驗結果 46
第五章  結論與未來發展 52
5.1 結論 52
5.2 未來發展方向 53
參考文獻 55
附錄:英文論文 58

圖目錄
圖1.1 美國911恐怖攻擊事件之新聞照片 1
圖1.2 靜物異狀之實例照片 4
圖2.1 影像直方圖範例 8
圖2.2 灰階轉換比較 9
圖2.3 8-neighbour structure element 12
圖2.4 連通區域標示範例 13
圖2.5 移動物體之分離狀況示意圖 11
圖2.6 背景時間差異法 12
圖3.1 監控場景內容之分類與組織架構 21
圖3.2 偵測靜物異狀之基本流程 23
圖3.3 靜物異狀偵測系統架構圖 26
圖3.4 以背景相減法擷取目前畫面影像中的前景物體 27
圖3.5 以背景相減法擷取動態場景之前景物體 28
圖3.6 背景相減法之示意圖 29
圖3.7 「以時間差異法原理為基礎的背景相減法」之示意圖 30
圖3.8 以投票法建構背景影像之示意圖 32
圖3.9 sliding buffer法概念圖 33
圖3.10 以sliding buffer法更新瞬時背景 34
圖3.11 以sliding buffer法更新瞬時背景 34
圖3.12 以sliding buffer法更新瞬時背景 35
圖3.13 以sliding buffer法更新瞬時背景 35
圖3.14 利用灰階轉換公式之示意圖 37
圖3.15 取R、G、B最大值進行灰階轉換之示意圖 38
圖3.16 灰階轉換之結果比較 39
圖3.17 利用Otsu法二值化處理之結果比較 40
圖3.18 經過侵蝕與擴張運算後之結果比較 41
圖3.19 進行連通元件標示後之結果比較 42
圖3.20 根據連通元件之資訊偵測出靜態前景物 43
圖4.1 靜物異狀偵測系統之執行畫面 45
圖4.2 測試影片二中被偵測到的滯留物 48
圖4.3 測試影片三中被偵測到的滯留物 48
圖4.4 測試影片四中被偵測到的滯留物 48
圖4.5 測試影片二 51
圖4.6 測試影片三 50
圖4.7 測試影片四 51

表目錄
表4.1 靜物異狀偵測結果 47
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