Improved Low Rank plus Structured Sparsity and Unstructured Sparsity Decomposition for Moving Object Detection in Satellite Videos

Junpeng Zhang, Xiuping Jia

Abstract: Detecting moving objects from satellite videos decomposes video frames into low rank background with additive sparse foreground, and moving objects are commonly rec-ognized by pixel-wise sparsity in the foreground. In prac-tice, moving objects are sets of spatially related pixels and tend to present structured sparsity rather than pixel-wise sparsity. Based on this spatial prior, Structured Sparsity Inducing Norm models moving objects as sparse sets of neighboring pixels, however, the isolated sparsity, which is unsuited to the spatial prior, is left to corrupt the low rank background. In order to address the corruption of unstructured sparsity to background, we proposed a new decomposition formulation named Improved Low Rank plus Structured Sparsity and Unstructured Sparsity Decomposition (ILRSUSD). An inexact Alternating Direction Method is then proposed to solve the improved decomposition formulation efficiently. Experimental result on a satellite video dataset demonstrates our improvement in background modeling with boosted moving object detection precision against state-of-art approaches.