複雜背景與低亮度之遺失物或遺留物偵測法

Missing Objects or Abandoned Objects Detection Method Under Low Brightness and Complex Background

譚正中(Jheng-Jhong Tan)

指導教授 ： 范育成

國立臺北科技大學/電資學院/電子電腦與通訊產業研發碩士專班/碩士(2010年)

摘要

目前在數位式監視系統的研究領域中，主要針對移動物體偵測、貴重物品偷竊偵測或是遺留物體為研究方向，但是衍生出來許多問題值得去思考，其中一個研究領域為低亮度環境下所產生的誤判，另一個研究領域為在複雜背景中如何不被干擾依然能偵測出移動物體，這些都值得我們去深入探討並解決的方向，在環境變化中我們要做建立背景模型來適應任何環境的變化，本文採用高斯混合模型來建立背景，首先需要適應低亮度，並且在複雜背景的環境下有效的偵測出遺失物體或是遺留物體，並且將被偵測出來的雜訊使用形態學濾波方式來過濾，最後再使用輪廓偵測演算法去判斷遺失物或是遺留物，應用方面可以在許多企業或百貨業領域中的貴重物品保全系統、軍事要地的防禦以及家中防盜方面來推廣，讓此監視系統成為貴重物品防盜與安全考量的重要一環。

關鍵字

數位式監視系統 ； 高斯混合模型 ； 形態學 ； 輪廓偵測

並列摘要

Moving object detection, stolen valuables finding, and abandoned objects searching, are current trends of research for digital surveillance systems. However, many problems are still not solved. One issue is misjudgment generated in low light environment, and another is how to detect moving objects under complex background. In order to adapt to any changes in the environment, we have to build background model. This study uses Gaussian Mixture Model to build the background model. At first, it has to adapt to low light and effectively detect missing objects and abandoned objects in complex background environments. Noises detected are filtered through morphology filter. Finally, contour detection algorithm is used to determine missing objects or abandoned objects. This kind of digital surveillance systems can be applied to security systems for valuables in department stores, company, defense of military places and home security and thus become an essential part of valuables security and safety considerations.

並列關鍵字

Digital surveillance system ； Gaussian Mixture Model ； Morphology ； Contour detection

參考文獻

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