摘要
移動物件追蹤，大都應用在即時監控系統中，在影片的前景中追蹤特定的移動物件。一般都是利用先前取得的背景做背景相減法將影像減去背景而得到一個較為粗糙的前景。由於物件的隨意移動，周遭光線的變化或物件移動產生遮蔽等因素，在處理過程中不容易取得乾淨背景，因而我們的系統採用GMM(Gaussian Mixture Model, GMM)來建立背景，GMM是利用高斯能任意逼近任何曲線的特性來建立，將影像上每一點隨時間觀測到的色彩資訊序列當成一個隨機序列X的取樣，則該點可能的色彩資訊，可透過將多個高斯機率分布組合的模型來表示。一般而言，背景的色彩資訊比前景的色彩資訊在時間軸上停留總時間較為長久，因此便能定義適當的閥值，加以區別，進而建立背景。取得背景，利用背景相減法得到畫格中移動物件的區域(即前景)，系統再利用粒子濾波器(Particle Filter, PF)，進行追蹤。粒子濾波器一直是進行物體追蹤的一個有效方法；然而當特徵狀態空間維度很高時，其追蹤容易出現飄移的情況。由於與PF結合，GMM只需少數個高斯函式就可達到不錯的精確度，因此在提高GMM與PF的效率同時，也不影響整體精確性。利用GMM我們僅須建立背景就能較精確定位物件，並大量刪減所需計算的資訊，再利用PF的追蹤結果。
並列摘要
For object detection and tracking, we first obtain a background from a sequence of frames, and acquire a coarse foreground for the following frame by simply subtracting the background. However, it is difficult to obtain a clean background in practice due to the presence of moving objects, change of lighting condition, or object occlusion. To cope with such problems, we use modified version of Gaussian Mixture Models (GMMs) to perform background construction. The color information of each point in an image sequence may be considered as a random variable and thus it can be described by a combination of some Gaussian models. With the fact that background information stays longer than foreground information, GMMs distinguish background pixels and foreground pixels according to the maximum time the similar information staying on a pixel over a sequence of frames. As soon as a coarse foreground is obtained by background subtraction, we perform some operations, including shadow removal, edge detection, and connected component analysis, to localize each moving object in the foreground. As long as an object is detected, it is then tracked in the following frames by the use of Particle Filters (PF). PF is effective but the dimension of its state space is high so as the tracked objects tend to be shifting. To reduce this problem we modify the particle filtering by carrying out tracking over the foreground portion instead of the whole image. With the use of the modified versions of GMMs and PFs, our system was proved to have high accuracy rate of detection/tracking and satisfactory time efficiency.