Background subtraction in dynamic scenes with adaptive spatial fusing
Background subtraction in highly dynamic scenes has been a critical challenge for traditional pixel-wise background models which perform poorly when the background has dynamic textures. In this paper, we consider background modelling in a spatial perspective and make an attempt to exploit more information from the outputs of pixel-wise model. We propose a background subtraction scheme using adaptive spatial fusing to refine the output of typical pixel-wise background model — Mixture of Gaussians (MoG) and employ a MRF-MAP scheme to make foreground-background classification using the spatial correlation. Experiments on several challenge sequences show that our method is able to yield significantly better results than the traditional ones and is compelling with existing state of the art background subtraction algorithms. Additionally, we proved our algorithm has linear running time complexity and any pixel-wise background model could be easily integrated into our spatial fusing scheme which greatly enhanced its scalabilities and applications.