文章基本信息

标题：Incremental Eigenspace Model Applied to Monitoring System
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作者：Byung Joo Kim
期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2014
卷号：8
期号：5
页码：243-252
DOI：10.14257/ijsia.2014.8.5.22
出版社：SERSC
摘要：This paper describes a real time feature extraction for real-time surveillance system. We use incremental KPCA method in order to represent images in a low-dimensional subspace for real-time surveillance in the traditional approach to calculate these eigen space models, known as batch PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigen space is only possible when all the images must be kept in order to update the eigen space, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that incremental KPCA has similar accuracy compare to KPCA and more efficient in memory requirement than KPCA. This makes pro-posed model is suitable for real time surveillance system. We will extend our research to real time face recognition based on this research.
关键词：Incremental KPCA; Eigen space; Monitoring System