A Novel Moving Object Segmentation Algorithm Using Kernel Density Estimation and Edge Information

This paper proposes a novel moving object segmentation algorithm based on kernel density estimation and edge information to solve the color similarity problem between foreground and background.During the stage of foreground/background modeling,both color feature and edge feature are used to build two probability models.Under the framework of Markov random field(MRF),three energy terms associated with the likelihood of foreground/background,spatial continuity and temporal consistency are introduced to construct a graph,and the graph cut method is exploited to reliably segment moving objects.Experimental results demonstrate that the proposed algorithm reduces the segmentation error when foreground and background show similar colors,and greatly enhances the segmentation robustness during the whole video sequence.