基於等位函數法之運動物體偵測與追蹤

Abstract

監控系統中的單一攝影裝置通常包含建立背景模型、偵測運動物體、追蹤運動物體等三個步驟。在本論文中，我們將討論如何在這些步驟當中，運用等位函數法來記錄運動物體的輪廓。在整個運動偵測與追蹤的過程中，我們首先建立環境模型以利於使用「背景相減法」來達成運動物體偵測。當使用動態攝影機來追蹤運動物體時，由於背景資訊會隨時間改變，此時需要改採「區塊追蹤模型」才能持續追蹤運動物體。為了減低背景環境的干擾，原始的區塊追蹤模型會被加以修改，以考慮前後兩張畫面等位函數曲面之間的交互關係。此外，利用畫面中統計特性加入機率預測的模型，可增強區塊追蹤的強韌性。論文最後會提出一套整合的監控系統架構，架構中的不同元件會選擇採用適當的輪廓模型來分別解決運動物體偵測與追蹤的問題。

An intelligent video surveillance system usually performs the tasks of background modeling, motion detection, and tracking. In this thesis, a level set function is used to record the moving objects for these three operations. The background model is first constructed before the background subtraction is performed for the motion detection. Then, a mobile camera keeps tracking the moving objects with a region tracking model. The original region tracking model is modified to alleviate the interference of cluttered environment. The relation between two level surfaces of successive two frames is taken into consideration. The probability model built from the statistic property of an image is also included. Finally, an integrated surveillance system is proposed. Different units in the surveillance system may choose appropriate contour models to solve their problems.