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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

has been cited by the following article:

• TITLE: Performance Evaluation of Various Functions for Kernel Density Estimation

  AUTHORS: Youngsung Soh, Yongsuk Hae, Aamer Mehmood, Raja Hadi Ashraf, Intaek Kim

  KEYWORDS: Background Model; Kernel Density Estimation; Kernel Functions

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.3 No.1B, January 22, 2013

  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.