期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Performance Evaluation of Various Functions for Kernel Density Estimation
1
作者 Youngsung Soh yongsuk hae +2 位作者 Aamer Mehmood Raja Hadi Ashraf Intaek Kim 《Open Journal of Applied Sciences》 2013年第1期58-64,共7页
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 b... 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. 展开更多
关键词 BACKGROUND Model KERNEL DENSITY ESTIMATION KERNEL FUNCTIONS
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部