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  • 學位論文

影像監視防盜保全系統之研製

Utilization of Video Surveillance and Monitoring System for Security Service

指導教授 : 王仲淳

摘要


本論文主要探討如何利用影像處理方法,建構以個人電腦為平台的監視防盜保全系統且兼具即時(Real-Time)、精確(Accurate)、自動化(Automated)、數位化(Digitized)的視訊監視系統( Video Surveillance and Monitoring , VSAM)。在程式的選擇上,本論文使用Microsoft Visual C++ 6.0來實現影像處理與使用者的介面,將攝影機所擷取到的影像傳送至電腦記憶體中進行影像處理及分析。系統處理流程,首先會先擷取監視區域正常狀態下的環境影像,建立標準對照影像。當環境有異常時,所擷取之影像產生變化,與標準對照影像相減後,可偵測異常狀態及位置,再運用入侵偵測及物件位移偵測來判斷監視的目標物狀況,若判定有物件入侵或目標物被取走,系統立即發出警示及警報,反應保全人員處理,達到抑制犯罪的目的,用以改善傳統視訊監視系統純錄影的缺點。

並列摘要


In this thesis, efforts are made to study image treatment system on a simple platform of PC (Personal Computer) to efficiently construct VSAM ( Video Surveillance and Monitoring ) with advantages of real time feedback, high accuracy, automatic and digital function. As to the chosen of programs, an idea is made in this project to utilize Microsoft Visual C++ 6.0 as the interface software between the image processing system and the user. Images captured by camera are transferred to random-access memory of PC to perform image treatments and analyses. As to the system processing procedures, one normal background image in the monitoring area is firstly captured to build as the standard reference image. Sequential images captured will alter when the background of the monitoring environment changes. After compared with altered images and the standard reference image, the extraordinary status and strange positions are detected. The status of monitored objects can be judged by detections of intruders and the displacements of monitored objects. When the comparing results reveal the appearance of intruders or the disappearance of monitored objects, the system will issue warnings and alerts to call security service personnel to achieve the goal of inhibition of crimes in real time. The disadvantage of simply saving the records in conventional VSAMs can be improved with the subject applications introduced in this thesis.

參考文獻


[1]J. A. Leese, C. S. Novak and V. R. Taylor. “The Determination of Cloud Motion Patterns from Geosynchronous Satellite Image Data,” Pattern Recognition, Clustering, Statistics, Grammars, Learning, Vol.2, pp. 272-292, 1970.
[2]K. P. Horn and B. G. Schunck, “Determining Optical Flow,” Artificial Intelligence, Vol.17, pp. 185-203, 1981.
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[4]R. T. Collins, A. J. Lipton, H. Fujiyoshi, and T. Kanade, “Algorithms for Cooperative Multisensor Surveillance,” Proceeding of the IEEE, vol. 89,no. 10, October 2001.
[6]M. Greiffenhagen, D. Comaniciu, H. Niemann, and V. Ramesh, “Design, Analysis, and Engineering of Video Monitoring System : An Approach and a Case Study,” Proceeding of the IEEE, vol. 89, no.10,October,2001.

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