[[alternative]]智慧型監控的停車場：人和車的偵測

[[abstract]]本文提出一個在智慧型監控的停車場中偵測人和車的方法。本方法包括快速背景相減法，移動物件行為分析，以及移動物件分類。在背景相減法中，無需事先建立背景模式影像，此背景模式影像會被一個以費式級數為基礎的快速背景更新法來更新，此快速背景相減法可以在固定式或移動式的攝影機下偵測移動物件。在移動物件行為分析中，提出一個移動分析表，來記錄移動物件的移動資料，利用這個移動分析表將移動物件分為五類：停止，移動，移動變停止，停止變移動以及攝影機位置改變，利用這五類來決定背景更新的速度。在移動物件分類中，利用人的膚色，頭髮，眼睛，嘴唇，以及移動物件的寬高比等特徵，加上規則式判斷條件，對移動物件確認是人或車子。經實驗證明，我們提出的快速背景相減法是有效的，每秒鐘可以處理13張320x240的畫面數，比文獻中的相關研究還要快。尤其是背景更新速度，更是到目前為止，有關背景更新的研究中，更新速度最快的，只要4個Frames，就可以完成背景更新動作。 This paper proposes people and car detection method in intelligent surveillance for parking lots. This method includes fast background subtraction, motion object behavior analysis, and motion object classification. The background subtraction method need not background model in advance. A Fibonacci Series based background updating is proposed to update the background model. The fast background subtraction is used to detect motion object under the fixed or movable camera. In the motion object behavior analysis stage, a motion analytical table is proposed to record the movable behavior. The motion analytical table is used to divide the motion object into five behaviors: stop, move, move and stop, stop and move, changing the camera position. These five behaviors are used to determine the speed of the background updating. In the motion object classification phase, human's skin, hair, eyes, mouth, and the ratio of the motion object are used to identify the motion object as people or car. The identification method is rule-based. The experiment shows that the fast background subtraction is effective 1 which can deal 320x240 frame size with 13 fps. Especially the speed of the background updating is the fastest than other methods. The speed of the background updating only uses 4 Frames