  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-static
background, and thereby they may have trouble in coping with the background
scenes that exhibit a persistent dynamic behavior. In this work, we shall
introduce two techniques to fill in the gap. First, instead of using the raw
pixel-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 various
background changes and can therefore distinguish effectively the foreground
objects from the background. Then, to obtain more accurate results, we further
propose a Segmentation Constrained RPCA (SC-RPCA) model, which incorporates the
temporal and spatial continuity in images into RPCA. The inference process of
SC-RPCA is a group sparsity constrained nuclear norm minimization problem,
which is convex and easy to solve. Experimental results on seven videos from
the CDCNET 2014 database show the superior performance of the proposed method.

Computer Vision and Pattern Recognition