Traditional background subtraction methods perform poorly when scenes contain dynamic backgrounds such as waving tree branches, spouting fountain, illumination changes, camera jitters, etc. In this paper, from the view of spatial context, we present a novel and effective dynamic background method with three contributions. First, we present a novel local dependency descriptor, called local dependency histogram (LDH), to effectively model the spatial dependencies between a pixel and its neighboring pixels. The spatial dependencies contain substantial evidence for differentiating dynamic background regions from moving objects of interest. Second, based on the proposed LDH, an effective approach to dynamic background subtraction is proposed, in which each pixel is modeled as a group of weighted LDHs. Labeling a pixel as foreground or background is done by comparing the LDH computed in current frame against its model LDHs. The model LDHs are adaptively updated by the current LDH. Finally, unlike traditional approaches using a fixed threshold to judge whether a pixel matches to its model, an adaptive thresholding technique is also proposed. Experimental results on a diverse set of dynamic scenes validate that the proposed method significantly outperforms traditional methods for dynamic background subtraction.
Background subtraction in dynamic scenes using the dynamic principal component analysis
