在許多視覺式監控系統中，通常藉由建立背景影像，再將輸入的影像與背景影像做比較，以擷取前景物 。本研究所考慮的為動態環境，亦即環境中同一位置可能會有多種不同亮度及顏色展現的背景，而且環境中的光線也會隨時間而有所變動。要建置動態環境之背景，通常採用調適性高斯混合模型（Adaptive Gaussian Mixture Model），利用多個高斯函數來描述反覆出現的多種背景值，並透過函數參數值的調適，以適應光線所產生的變化。然而，已往的背景模型都沒有把陰影當做背景的一部分，使得陰影常會被當成前景物擷取出來，造成應用上的錯誤。本研究的目的，即在於建立可調適性背景與陰影的結合模型，以便在動態環境中，利用此模型擷取出沒有陰影的前景物影像。
建立此模型的主要流程分為兩部分，首先為陰影偵測：利用陰影亮度的物理性質篩選出可能是陰影的像素點，再將這些像素點依照反射率分成多個區塊，然後計算每個區塊的真實顏色向量；由於真實顏色向量已移去光線所造成的影響，因此可直接與資料庫中的背景顏色向量比對，以判斷出哪些區塊是背景卻被陰影覆蓋的區塊。第二部分則是建立陰影的顏色分佈：若一像素點被判定在陰影區塊中，便在其背景高斯混合模型中， 以額外的高斯函數描述該像素點的陰影顏色分佈 。建立出背景與陰影的整合模型，除了可用於擷取影像中的前景物，還可以用於偵測場景中的陰影部份，以便了解場景中的光線資訊，用途相當廣泛。
關鍵字：動態環境、調適性高斯混合模型、前景擷取、陰影偵測

In this paper, we integrate shadow information into the background model of a scene in an attempt to detect both shadows and foreground objects at a time. Since shadows accompanying foreground objects are viewed as parts of the foreground objects, shadows will be extracted as well during foreground object detection. Shadows can distort object shapes and may connect multiple objects into one object. On the other hand, shadows tell the directions of light sources. In other words, shadows can be advantageous as well as disadvantageous. To begin, we use an adaptive Gaussian mixture model to describe the background of a scene. Based on this preliminary background model, we extract foreground objects and their accompanying shadows. Shadows are next separated from foreground objects through a series of intensity and color analyses. The characteristics of shadows are finally determined with the principal component analysis method and are embedded as an additional Gaussian in the background model. Experimental results demonstrated the feasibility of the proposed background model.

Keywords: Dynamic scene, Adaptive Gaussian Mixture Model, Foreground detection, Shadow detection

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