隨著時代的進步，安全也隨著被大家所重視。然而在現今社會裡，自動化與智慧型的系統也成為目前科技所追求的。監控系統的環境可應用在各個環境下，例如機場，高速公路以及校園等等…。在這些環境中，常會有複雜的情形發生，例如多人交錯、多人並肩走在一起或者人的靜止，這些情況在追蹤都會是一個很嚴重的問題。因此如何有效解決以上所提的三大問題在追蹤這方面一直是很重要的議題。
在此論文中，為了解決含有以上所述的一些問題，擷取重要的幾何特徵就成為了達到精確的追蹤系統的主要方法之一。擷取幾何特徵的方法運用一個簡單且直覺的影像處理所獲得。然而幾何擷取特徵的特性含有唯一性及具有意義的。另外，在追蹤的系統上，運用這兩個重要的幾何特徵，並行的使用追蹤演算法，增加追蹤的準確度。最後在靜止物件上，同樣使用了這樣的幾何特徵，解決靜止物件所產生的相關問題，減少物件遺失的情形。實驗顯示提出的方法在上述的三個問題都能有好的結果。除此之外，為了顯示提出方法有效的改善問題，將結果轉為數據，並與另外兩個方法做比較。實驗的影片也採用被採信的基準影片PETS2001、PETS2006和PETS2007。
除了軟體上的開發，也將其實現在DM642開發平台上，此系統利用一監視器傳輸到DM642開發板上進行追蹤處理，再經由液晶螢幕顯示出來。在DM642平台上跑了一實例來驗證結果，並評估其效能。

Tracking multiple targets in complex situation is challenging. The difficulties are tackled multiple targets with occlusions, especially when multiple involved targets are grouped, moving together in appearance ,and target is static. These problems are still focused in recent years.
In this paper, we present a multiple targets tracking system for the management of occlusion problem. The proposed algorithm introduces a geometric shape co-tracking strategy. It decomposes targets into geometric shapes located on body and head parts based on reasonable target geometry consideration. Features selected from the decomposed geometric shapes then can be used to track targets through intersections such as occlusion. Projection histogram and ellipsoid shape model are adopted to manage decomposed geometric shapes corresponding to each target. Tracking is done through Kalman filtering process with high efficient and low complexity issue. The problem of sleeping object is handled by mean shift with no foreground needed. Experimental results show that the occlusion of grouped targets can be tracked successfully on recent challenging benchmark sequences.
The proposed method is also implemented in DM642. The input video is from a camera transfer to DM642, then the input video has been through tracking process and output the video in LCD. To verify the system, a realistic video has been shown also.

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