Real-time Dynamic Background Segmentation Based on a Statistical Approach
學年 97
學期 2
發表日期 2009-03-26
作品名稱 Real-time Dynamic Background Segmentation Based on a Statistical Approach
作品名稱(其他語言)
著者 Peng, Jian-wen; Horng, Wen-bing
作品所屬單位 淡江大學資訊工程學系
出版者 IEEE Systems, Man, and Cybernetics Society
會議名稱
會議地點 Okayama, Japan
摘要 Background modeling is usually the first step in vision-based surveillance systems. Subsequent foreground segmentation can then be performed by comparing the variations between the current image and the reference background of the monitored scene. Various approaches have been proposed to deal with this issue. They differ in the type of background models used. However, these approaches emphasize only what the distribution of the background looks like, not what the real actions of the background are taken place during some period of time. In this paper, we propose a real-time background model that can automatically self-adjust to the scene changes. The experimental results show that the proposed background model has better performance over others in terms of noise suppression and the preservation of foreground details. In addition, our model can also operate correctly at night. Furthermore, it can effectively resist shaking of cameras and objects.
關鍵字
語言 en
收錄於
會議性質 國際
校內研討會地點
研討會時間 20090326~20090329
通訊作者
國別 JPN
公開徵稿
出版型式 紙本 電子版
出處 Proceedings of the 2009 IEEE International Conference on Networking, Sensing and Control, pp.404-409
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