Abstract
This paper describes a feature extraction method for realtime surveillance. Eigenspace models are a convenient way to represent set of images with widespread applications. In the traditional approach to calculate these eigenspace models, known as batch PCA method, model must capture all the images needed to build the internal representation. This approach has some drawbacks. Since the entire set of images is necessary, it is impossible to make the model build an internal representation while exploring a new person. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. In this paper we propose a method that allows for incremental eigenspace update method by incremental kernel PCA for realtime surveillance. Experimental results indicate that accuracy of proposed method is comparable to batch KPCA and outperforms than APEX. Furthermore proposed method has efficiency in memory requirement compared to KPCA.
This study was supported by a grant of the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (A05-0909-A80405-05N1-00000A).
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, BJ., Lee, CB., Kim, IK. (2006). A Robust Realtime Surveillance System. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_34
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DOI: https://doi.org/10.1007/11815921_34
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