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Due to its efficiency and stability, Robust Principal Component Analysis(RPCA) has been emerging as a promising tool for moving object detection.Unfortunately, existing RPCA based methods assume static or quasi-staticbackground, and thereby they may have trouble in coping with the backgroundscenes that exhibit a persistent dynamic behavior. In this work, we shallintroduce two techniques to fill in the gap. First, instead of using the rawpixel-value as features that are brittle in the presence of dynamic background,we devise a so-called Gaussian max-pooling operator to estimate a"stable-value" for each pixel. Those stable-values are robust to variousbackground changes and can therefore distinguish effectively the foregroundobjects from the background. Then, to obtain more accurate results, we furtherpropose a Segmentation Constrained RPCA (SC-RPCA) model, which incorporates thetemporal and spatial continuity in images into RPCA. The inference process ofSC-RPCA is a group sparsity constrained nuclear norm minimization problem,which is convex and easy to solve. Experimental results on seven videos fromthe CDCNET 2014 database show the superior performance of the proposed method.

Detection of Moving Object in Dynamic Background Using Gaussian Max-Pooling and Segmentation Constrained RPCA