在本論文中，我們利用渾沌進化演算論(chaos evolutionary programming)產生一個有效影像序列軌跡為基礎的點追蹤演算法。對傳統追蹤影像中目標物的運行限制，如不變形固態或較小的運動變化已有先前的方法來解決。然而提出的演算法可以印証出在連續的影像序列上追蹤目標的點相似性有較好的效率。它以chaos evolutionary programming 為主要架構，建立含運動模組、物體縮放標準與多項式插補應用的準則為輔，使得我們的追蹤器能夠適用於目標物在旋轉、位移、遮蔽物、縮小與放大狀態下可以準確地追蹤目標。其中，運動模組是用來定義運動軌跡的法則;而物體縮放標準主要是以雙線性內插法來針對目標物發生形體縮放狀態的解決方式，另外，提出的多項式插補的用途，主要是針對其特性用來重建完整的追蹤軌跡，因而當遮蔽物出現時，追蹤器亦能準確的估測目標位置，同時也有效的改善追蹤所需花費的時間。我們的實驗數據顯示此提出的演算法可以在各種不同的環境狀態下得到準確的結果。

A trajectory-based point tracker using chaos evolutionary programming (CEP) algorithm is proposed in this thesis. While motion constraints such as rigidity and small motion which are imposed by previous approaches are liberated, the proposed CEP is proved to be effective for establishing point correspondence between two consecutive frames sampled at a fixed interval. The whole point trajectory within the sample interval is then reconstructed by polynomial interpolation. Our experimental results demonstrate that the proposed point tracker can accurately locate target under different kinds of situations like object deformation, occlusion, and sudden motion as well.

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