Performance Evaluation of Various Functions for Kernel Density Estimation


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.

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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. 1B, 2013, pp. 58-64. doi: 10.4236/ojapps.2013.31B012.

Conflicts of Interest

The authors declare no conflicts of interest.


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