隨著科技的發展，取像設備價格的降低及普遍性提高，監控系統目前已經廣泛的應用在日常生活當中，一般大眾開始願意在家中裝設保全系統，藉以保護家人和財產的安全。但一般監控系統若稍有些許環境的影響(例如:風速過強…等)造成畫面的變化，就會觸動保全系統，過於頻繁的錯誤警報，久之便降低對於保全系統警報所應有的警覺性及靈敏度。
本論文結合影像處理中及加入倒傳遞類神經網路的技術，建構一套行人偵測系統，進以判斷畫面中是否有入侵者。首先，運用高斯混合模型建立動態背景，以利於取出畫面中的移動物體。接著，利用移動物體形狀的資訊計算區域比對(chamfer distance)及結合事前利用類神經網路訓練過後的資料來判斷出移動物體是否為人或是其他動物或其他影響之變因，若系統判斷移動物體為人則發出警告給監控者。最後由實驗結果證明，本論文實做之方法有一定程度的辨識率。

Due to the fast development of computer and video technologies and the cost-down of capturing devices, surveillance systems are widely applied in our daily life. People use the home security system to protect their family and property. However, most security systems will send alarm messages to users when the sensors were triggered, but cannot identify what the intruder is. If the security systems often make the false alarm caused by animals, people may relax their vigilance as time pass. In order to solve those common problems of traditional home security systems, we combine the image recognition, motion detection, image processing, and Neural Network to build a system that can identify whether the illegal intruder is human or other animal. First, we construct the background map to obtain the foreground by using Gaussian mixture model (GMM). Then, we combine shape-based detection and Back-propagation Neural Network to determine whether the foreground is human or not. If the foreground is human, the system will issue an alarm. Some experimental results were demonstrated to verify the performance of the proposed method.

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