中 文 摘 要
　　一般而言，視覺追蹤系統主要可分成靜態偵測與動態追蹤。在靜態偵測部分，除要能準確地分割出移動的目標物之外，還須進一步的判別是何種目標物類型，以提供交通或是保全監控之用。在眾多學者所提出之相關分類演算法中，本論文採用階層式分類演算法進行移動目標物之分類，此方法能夠即時且準確地分辨出移動目標物之類型，並於最後之實驗結果證實該方法能達到90%以上之準確率。而在動態追蹤方面，本論文使用多重影像特徵比對演算法，該法結合主動式輪廓模型、色彩長條圖比對法、樣板比對法、輪廓比對法與位置預測等資訊做為動態追蹤之相似度量測，以提高動態追蹤之準確性。其中為提升位置預測資訊之準確性與降低因視覺回授而產生之延遲現象，本論文提出以模糊邏輯之調整方式進行g-h filter參數之調整。此外為驗證本論文所提方法之可行性，分別進行數項靜態偵測與動態追蹤之實驗。

ABSTRACT
　Generally speaking, a visual tracking systemconsists of two components— static detection and dynamic tracking. The two major tasks of static detection are moving object detection and classification. The results of moving object classification are particularly useful in security/surveillance and traffic monitoring applications. Among existing moving object classification algorithms, the hierarchical classification is employed in this study due to its superior classification ability. Dynamic tracking uses a multi-cue matching approach in this study to perform dynamic tracking of a moving object. The image cues used in this study include active contour modeling, template matching, color histogram matching, contour matching, and target position prediction. In order to improve the accuracy of target position predication and reduce visual feedback delay, a fuzzy logic based parameter tuning approach for g-h filter is proposed. Finally, several static detection and dynamic tracking experiments are performed to verify the effectiveness of the proposed algorithm.

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