在視訊監控、機器人視覺的應用，物件偵測與追蹤扮演很重要的角色，如何建立一個穩定的監控平台一直是大家持續研究的目標。然而這些監控設備常礙於硬體本身的限制，通常都會有監控範圍不夠廣泛，或產生死角等等的問題；為了改善此情形，某些監控設備會使用超廣角鏡頭、使用數位PTZ攝影機來改善視角問題、或是使用多攝影機架構，而提出這些方法都是為了要增加增廣監控的範圍，使其視域變廣達到全面監控的效果。
　　本論文針對於多攝影機視訊監控應用，提出一個強健且高效率的多攝影機協同追蹤的方法，比起單一攝影機系統的監控，可以達到更全面的監控範圍；擴大了監控區域、也增加了監控的可視角。我們先利用漸進式背景建模，標定監控重疊區域後，在利用連通物件方法分析取得前景物後，以PSO演算法進行適應性的追蹤加上錯誤校正機制，而當追蹤物件進入重疊區域即將離開當下攝影畫面時，多攝影機間透過一個換手協議來決定轉移追蹤權，以達到可靠、強健的多攝影機連續追蹤監控。我們同時提出一個物件追蹤的性能評估方法，用以評估本研究所設計的多攝影機視訊監控系統性能。
　　現今研究的多攝影機追蹤方法，多著重於重疊區物體深度資訊的取得，本方法則朝向增廣其監控範圍，此外也不需要複雜的場景參數設置，即可實現多攝影機連續物件追蹤的視訊監控應用。

　　In the application of security surveillance and robot vision, object detection and tracking always play an important role. How to establish a stable intelligence surveillance platform is a final purpose we all want to accomplish. But we usually face the problems of the narrow angle of view and dead space in single camera. So we may use some wide-angle lens, PTZ (pan-tilt-zoom) camera, or even multi-camera system to improve this kind of issue.
　　In multi-camera surveillance system, we proposed a robust and efficient mechanism to solve the foregoing problem. At first we use the progress background modeling and calibrate the demarcation (overlapped area) between the cameras, and the connected-component to extract the interest object of the foreground, and then put a PSO tracker on that. When this tracking object is about to leave the current camera, we design a cooperative protocol to take over the tracking token and go on tracking the object in another camera. The cooperation between cameras can achieve reliable and robust in continuous tracking. Finally we proposed a NGT measure to evaluate the tracking performance.
　　Compares to the other multi-camera approaches, most of them prefer to put the cameras toward the overlapped area, in order to get the depth information as a criterion. We tend to get more surveillance view instead of the overlapped area. And with the uncomplicated setup of the camera calibration, we can realize the intelligent surveillance for multi-camera cooperative object tracking.

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