Application of Grey Prediction to Progressive Privacy Protection in Surveillance Systems

碩士 === 朝陽科技大學 === 資訊工程系 === 102 === As the technology of digital cameras advances significantly, more and more cameras have been applied to surveillance systems, such as the traffic management in a city and the safety of public spaces in our living environment. When the surveillance systems monitor...

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Bibliographic Details
Main Authors: Chih-Wei Liu, 劉志偉
Other Authors: Cheng-Hsiung Hsieh
Format: Others
Language:zh-TW
Published: 2014
Online Access:http://ndltd.ncl.edu.tw/handle/27364417700108975636
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Summary:碩士 === 朝陽科技大學 === 資訊工程系 === 102 === As the technology of digital cameras advances significantly, more and more cameras have been applied to surveillance systems, such as the traffic management in a city and the safety of public spaces in our living environment. When the surveillance systems monitor the illegitimate behavior, they also reveal the people’s privacy. The more extensive applications of the surveillance systems result in the more attention to the threats to the personal privacy by the surveillance systems. This thesis applies the minimum filter to the privacy protection and uses the grey prediction model to detect moving objects. In the proposed approach, a frame is partitioned to several areas with different user-defined degrees of security. When a moving object is in the alarm area, the corresponding partitions show the object clearly. For other case, different privacy protections are provided when it moves out the alarm area. By this doing, the goal of progressive privacy protection is achieved.