OJAppS  Vol.3 No.1 B , March 2013
Performance Evaluation of Various Functions for Kernel Density Estimation
ABSTRACT
There have been vast amount of studies on background modeling to detect moving objects. Two recent reviews[1,2] showed that kernel density estimation(KDE) method and Gaussian mixture model(GMM) perform about equally best among possible background models. For KDE, the selection of kernel functions and their bandwidths greatly influence the performance. There were few attempts to compare the adequacy of functions for KDE. In this paper, we evaluate the performance of various functions for KDE. Functions tested include almost everyone cited in the literature and a new function, Laplacian of Gaussian(LoG) is also introduced for comparison. All tests were done on real videos with vary-ing background dynamics and results were analyzed both qualitatively and quantitatively. Effect of different bandwidths was also investigated.

Cite this paper
Y. Soh, Y. Hae, A. Mehmood, R. Hadi Ashraf and I. Kim, "Performance Evaluation of Various Functions for Kernel Density Estimation," Open Journal of Applied Sciences, Vol. 3 No. 1, 2013, pp. 58-64. doi: 10.4236/ojapps.2013.31B012.
References
[1]   M. Piccardi, “Background subtraction techniques: a re-view,” 2004 IEEE International Conference on Systems, Man, and Cybernetics, 2004, pp.3099-3104

[2]   Y. Benezeth, P. Jodoin, B. Emile, H. Laurent, and C. Rosen-berger,“Review and evaluation of commonly imple-mented background subtraction algorithms,” 19th Inter-national Conference on Pattern Recognition, 2008 ,pp.1- 4

[3]   C. Wren, A. Azarbayejani, T. Darrel, and A.Pentland,“Pfinder: real-time tracking of the human body,” IEEE Transactions on Pattern analysis and Ma-chine Intelligence, vol.19, no.7, 1997, pp.780-785.

[4]   P. Wayne Power, Johann A. Schoo-nees, “Understanding Background Mixture Models for Foreground Segmentation,” Proc. Image and Vision Computing New Zealand, 2002.

[5]   A. Elgammal, R. Duraiswami, , D. Harwood, and L. Davis, "Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance,” Proc. of IEEE, VOL. 90, NO. 7, 2002

[6]   N. Oliver, B. Rosario, and A. Pentland, “A Bayesian computer vision system for modeling human interactions,” IEEE Transactions on Pattern analysis and Machine Intelligence, vol.22, no.8, 2000, pp.831-843.

[7]   W. Zucchini, Applied smoothing techniques, Part 1 Kernel Density Estimation., 2003

[8]   M. P. Wand ,M. C. Jones, Kernel Smoothing, Monographs on Statistics and Applied, Probability Chapman & Hall, 1995

[9]   http://mmc36.informatik.uni-augsburg.de/VSSN06_OS

 
 
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