論文中，我們將基於Kinect之靜／動態機車車牌辨識系統分為：深度資訊提取、車牌定位、文字辨識三個階段處理。 首先主要提取Kinect所偵測前方機車車輛的深度資訊，即可解決複雜背景等問題，並且再利用演算法將多機車之影像依序分割並提取為單一機車之影像進行後續處理。接著利用Marker Detection搜尋方框物件的特性定位車牌，而此演算法優於可即時偵測並定位以及擁有良好的抗旋轉、變型、歪斜與遮蔽等強健性。 最後將定位車牌傾斜校正與字元切割後，並給予樣板比對進行字元辨識。待測之機車車牌分為靜態與動態兩種狀況，動態為行進間拍攝前方行駛中機車車輛進行偵測，而靜態則是以行進間偵測路邊停放之車輛。而本系統以靜態偵測為主要研究目的。 In this paper, we divide the system into three parts: the extraction of the depth information, the orientation of the license plate and the identification of the words. First, we exact the depth information of the license plate on the motorcycle in front of the Kinect to solve the problem of the complex background. Then we use the algorithm to divide the image of the motorcycles sequentially and abstract the image of one motorcycle to do the following process. After that, we apply the Marker Detection Algorithm with the characteristic of searing the square object to orientate the license plate. The algorithm can not only detect in the real-time but also have the robust resistance to rotation, deform, slope and shelter, etc. Finally, we correct the slope of the retaining license plate and divide the words. We base on the sample to identify the words. The training motorcycle license plates divide into the static and dynamic state. The dynamic state is to detect the driving motorcycle license plate under moving. But, the static state is to detect the parking motorcycle in the roadside under moving. However, our system’s main purpose is the static detection.