| 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。
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. |
