Youngsung Soh, Yongsuk Hae, Aamer Mehmood, Raja Hadi Ashraf, Intaek Kim
Department of Information and Communication Engineering, Myongji University, Yongin, 449-728, Korea.
DOI: 10.4236/ojapps.2013.31B012   PDF    HTML     4,820 Downloads   8,430 Views   Citations

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.

Keywords

Background Model; Kernel Density Estimation; Kernel Functions

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Conflicts of Interest

The authors declare no conflicts of interest.

References

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