公共場合的安全問題在現今的社會裡顯得越來越重要，而其中又以交通安全為首要，因此如何利用現有的監視系統資源來有效的獲取交通資訊是很重要的一個課題。在本篇論文當中提出了一個應用於交通的數位監視系統上之車輛分類系統，此系統能在經過追蹤車輛的過程後，正確的將各個車輛分類出來，以得到監視系統中所出現車輛的相關資訊。而這個演算法不僅僅可以將外觀差很多的物體分辨出來，甚至連外觀差不多的物體也可以被分辨出來，例如腳踏車和機車等等。首先我們先利用相減的方法來將我們感興趣的物件擷取出來，接下來我們也會對這些物件作一些型態學上的處理讓擷取出來的物件更具有準確性，最後便利用這些擷取出來物件的特徵，進而分辨出它們是屬於何種類型的車輛，達到車輛分類的效果。

Recently, the social security problem in public places becomes more and more important, and the traffic safety problem is particularly one of the most important of all. Therefore how to get the efficient traffic information from the limited surveillance resources is a very worth discussing topic. The Thesis proposes a pedestrian and vehicle classification algorithm for digital traffic surveillance systems. After the stage of detecting foreground moving objects, all kinds of vehicles are classified correctly by the proposed method; finally the information of each vehicle which appears in the scene is obtained easily. The classifier can classify not only the objects with obvious differences in appearance but also those which are similar in their appearance, such as bikes and scooters. First of all, background subtraction is used to get the foreground objects which we are interested in, and then some morphological operations are done to make the foreground objects smoother and more precise. Finally the feature information which is obtained from the foreground objects are well used to classify all kinds of vehicles that are driven on the road in daily life.

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