對於一視覺追蹤系統而言，如何準確且快速地偵測出畫面中的移動目標物並進行追蹤，一直是相當重要的研究課題。在移動目標物偵測方面，背景相減法由於其計算量少且偵測結果佳而被廣泛地使用。然而，該方法仍有對光源變化敏感及背景初始化等問題，使得該法在複雜的環境下無法準確地進行偵測。有鑑於此，本文提出改良式適應性背景相減法，藉由動態地更新背景模型來維持偵測結果的準確性。在目標物追蹤方面，SSD法一直是視覺伺服領域中經常使用的影像追蹤技術。然而該方法對於目標物的外形變化過於敏感，因而難以被應用於真實環境的追蹤應用中。為了克服此一困難，本文提出以多重影像特徵比對法來進行追蹤，藉由結合數種不同性質的目標物追蹤技術，來提升系統的適用性與強健性。本論文將所提之改良式適應性背景相減法以及多重影像特徵比對法運用於一自行建構之實驗用即時視覺追蹤系統，實驗結果顯示本論文所提之方法效果良好。

　　How to detect and track moving objects in video streams is an important and challenging research problem in visual tracking applications. Among the moving object detection algorithms, the background subtraction method has been widely used due to its less computational load and high detection quality. Nevertheless, with problems such as “sensitive to the changes of lighting” and “background initialization”, the background subtraction method often performs poorly in complex environments. To overcome this difficulty, a modified adaptive background subtraction method that can dynamically update the background model is proposed. On the other hand, the SSD method is a popular image tracking technique in visual servoing applications. However, the SSD method is very sensitive to the changes of target appearance. As a consequence, it may experience failures when applied to realistic environments. To overcome this difficulty, a multi-cue template matching algorithm that consists of several kinds of tracking algorithms is proposed to improve the practicability and robustness of the visual tracking system. To evaluate the performance of the proposed approach, the modified adaptive background subtraction method and the multi-cue template matching algorithm are applied to the real-time visual tracking system developed in our laboratory. Experimental results show that the proposed approach exhibits satisfactory performances.

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