在本論文中，開發出一個使用在固定全方位攝影機上的移動物體偵測系統，對環境進行全方位的監視，將移動的物體偵測出來，以克服攝影機死角的問題。系統會先對全方位攝影機進行校正動作，將正確的中心點找出。這裡使用了 Canny 邊緣偵測 (Canny Edge Detector) 與霍氏轉換 (Hough Transform)來找出正確的中心點出來。再來將輸入的全方位影像攤平為全景影像，使用全景影像來做移動物體偵測。
系統中提出了多重參數背景與前景模型之移動物體偵測，此方法使用了多重參數式背景模組來處理背景中週期性運動的物體，針對了前景也加入了多重參數式前景模組作為前景模型，來改善偵測前景時雜訊的問題。在實驗結果中，展示出本系統在不同環境下，可以對全方位攝影機拍攝到的畫面進行處理及移動物體偵測。

In this thesis, we present a motion object detection system on omni-directional camera. The system increases the fields of view in order to overcome the blind spot problem in surveillance system. First, we need to do omni-directional camera calibration to find the omni-directional image center; the Canny Edge Detector and the Hough Transform are used for this purpose. Next, we unwarp the omni-directional image to the panoramic image which is used for motion object detection. We propose the motion object detection using multiple parametric background and foreground models. This method can handle backgrounds with periodic motion and thus reduce the influence of noise. Finally, the experimental results show that the system can perform in different situations.

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