較常見的建立在像素觀點(pixel-wise)上的背景模型如高斯混合模型(GMM)或編碼書模型(Codebook Model)皆利用隨時間變化的像素資訊進行前背景的判斷，但該方式的模型對於周圍環境的考量卻較薄弱。
該背景模型將停留時間久且較相似的像素視為背景；而具有較大變異度的像素部份視為前景。然而，在前景和背景像素較為相似的環境中，以像素觀點的背景模型進行前景偵測時，容易誤判前景為背景而產生了前景破碎現象。尤其在低對比的環境下的移動物，其破碎情形更為明顯。
本論文利用環境中具較大對比度的未破碎前景資訊，提出以局部觀點針對低對比的破碎前景進行修正。各種場景的影片測試結果將可顯示本論文所提出的方法可對於低對比環境中破碎的前景進行改善。

Pixel-based dynamic background models, such as GMM or Codebook, are designed to capture the variation of a pixel’s value across time without considering the relationship between the pixel and its neighboring regions. When the pixel maintains a similar value for long enough, it is considered as a background pixel, and when its value experiences a large change, it is assumed caused by a foreground passing through. If some part of the foreground is with values similar to the background, the pixel-based models often result in foregrounds with broken segments, which are part of the actual foreground but falsely categorized as background. The broken foregrounds are often seen when the contrast between foregrounds and backgrounds reduces to below some value.
This thesis exploits the fact that a foreground appears without broken segments when its contrast to the background is sufficiently large, and proposes a component-based method to compensate the broken segments at low contrast cases using the foreground captured at high contrast occasions.

Experiments on videos of various scenes show that the proposed method can improve the segmentation and detection of foregrounds, especially in low contrast scenes.

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