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- W2384290326 abstract "For non-parametric kernel density estimation information redundancy and repetition computation in the training stage estimate error and large amount of calculation in the estimated phase,this paper proposed a method of clustering difference image kernel density estimation for foreground object detection.We first choose those samples that have higher frequency and diversity to contain important information based on max-min distance clustering in training sequence.A Gausisian KDE is built to estimatea motion object after adaptive threshold image difference calculation.Experimental results were given to demonstrate that the proposed algorithms are elimination of the typical non-movement noise point for estimated error and improving real-time capability." @default.
- W2384290326 created "2016-06-24" @default.
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- W2384290326 date "2009-01-01" @default.
- W2384290326 modified "2023-09-23" @default.
- W2384290326 title "Clustering Difference Image Kernel Density Estimation for Foreground Object Detection" @default.
- W2384290326 hasPublicationYear "2009" @default.
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