近十年來，隨著視訊產品的普及化及和電腦視覺技術的快速進步，使得動態影像偵測與追蹤的方法被應用在各種領域，例如監視系統、智慧型交通系統、停車場管理系統等，它們可用來取代許多無聊且費時的工作，也可避免因為人類的疲倦所帶來的疏失，而在時效上，它們具有即時回報突發狀況的能力，所以能大幅降低整體系統的時間成本。本論文所提的視覺偵測與追蹤系統，在偵測階段，共分成背景建立、前景偵測、陰影去除和背景更新四部份，其中背景建立部份，係採用中位數的方法來建立，而前景偵測部份，則使用擷取函數以間接相減的方式分辨前景與背景，另在去除陰影部份，是以決定性無模型為主的方法來除陰影，至於背景更新部份，則使用歷史圖來記錄背景變動的次數，進而達到更新背景的目的。又在系統的追蹤階段，本論文是採用粒子濾波器的方法來追蹤目標物，其中選取顏色分佈來當作目標物的特徵，而目標物的顏色記錄，則使用顏色機率直方圖來表示，並利用背景資訊來增加其權重，以獲得更好的追蹤效果。實驗結果顯示：在針對多目標物的一般情況下，我們的系統可以達到即時處理的速度，並且擁有良好的強健性。

In the last decade, due to the popularization of video products and the rapid development of computer vision techniques, the detection and tracking methods for dynamic images have been widely applied in many kinds of fields, such as video surveillance, intelligent transportation, and parking area management systems. They can replace a lot of bored and time-wasting work, and avoid mannal mistakes caused by fatigue of human. On the effectiveness for a given period of time, these visual detection and tracking systems possess the ability of reporting sudden situations in real time, so that the whole time costs of such systems can be greatly reduced. In this thesis, the detection phase of our developed system consists of four parts: background generation, foreground detection, shadow elimination, and background maintenance. In the background generation part, the median method is used for constructing background images from the past N frames. In the foreground detection part, an extraction function is applied to indirectly perform differencing to obtain foreground images. In the shadow elimination part, a deterministic nonmodel-based method is adopted to remove shadows. As to the background maintenance part, a history map which records the number of times of the changes of corresponding pixels is employed to maintain background images. In the tracking phase of the system, this thesis exploits a particle filter to track moving objects. The color distribution of a moving object is chosen as its features represented by a color probability histogram. In order to raise the accuracy of tracking, the background information serves as the increase candidate weight of a moving object. The experimental results reveal that in general situations our system can achieve real-time processing and can obtain robust detection and tracking results for multiple moving objects.

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