想像您坐在監視系統所在的警衛室，或站在出入口位置。試著要數進出的人數。這種工作只會令人厭煩、讓人昏昏欲睡，甚至造成數錯人數的錯誤。在這裡我們將焦點集中到出入口附近的人，他們是進去、出來、還是四處看看而已。
第一、我們利用 auto-variable codebook 的方法建立背景模型，將前景剪出。此方法根據影像像素出現的次數，判斷是否為背景。因此即使監視畫面上有物體在不斷移動，還是能建立完整的背景。此方法能解決影子、樹葉晃動、玻璃閃爍的影響，將影像中的前景清晰的剪出來。第二、在出入口處一定的範圍內設置ROI（感興趣的區域），此ROI才是我們接下來的步驟偵測的範圍，再定義出入口的界線。第三、在ROI範圍內利用可調整的目標樣板作比對，把樣板比對結果最低且低於門檻的範圍框起來並記下框底部的中心點。第四、在框框上半部1/3附近，利用Hough Transform 偵測頭部的輪廓，確認樣板比對的物體是方的還是圓的。第五、利用此物體前後的位置判斷在某特定範圍內的距離，這個中心點是不是可能為同一個人所有。若是，計算顏色的差異性卻是否為同一人所有。若否，則判定是新進入的人，並持續作物體追蹤。第六、將屬於同一個人的框底部中心點依序連接起來畫成軌跡。最後我們利用此軌跡跟出入口的界線來判斷此人是進去、出來、還是只是經過門口。
此系統能追蹤在ROI出現的物體，並持續追蹤不同的物體。依據每個物體的軌跡，判斷它的移動方向。

This thesis proposes a vision-based people counting system that can count the number of persons getting in or out of the entrance of a building.
First, we develop a so-called adaptive AV codebook background model and use background subtraction to divide foreground object from background in RGB color-space. In ROI (range of interest), we use the template matching method to find the objects, and then apply Hough transform to detect head contours to verify whether the object is a person. Second, we use a white rectangle to locate personal location and record the bottom center point as the trajectory point. To track the moving object, we compute the color distance between the previous and current tracked object, and link the center points which belong the same person as the trajectory of movement. Finally we analyze the trajectory to determine whether the person enters or exits or just passes by the entrance. Finally some experimental results are illustrated to verify our system.

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