LRSDSFD: low-rank sparse decomposition and symmetrical frame difference method for moving video foreground-background separation

Abstract

In scenes with dynamic background or measurement noise, the low-rank sparse decomposition background modeling algorithm based on kernel norm constraint is easy to separate the moving background or noise as part of the foreground and the foreground at the same time, and it has poor modeling performance for complex background. In order to solve this problem, this paper proposes a low-rank sparse decomposition and symmetrical frame difference method for moving video foreground-background separation. Firstly, low-rank sparse decomposition is used to constrain the background matrix. Secondly, the moving objects in the region of interest (ROI) are extracted by symmetrical frame difference method, and the background image is obtained by block background modeling. Numerical experiments show that compared with other five main algorithms, the proposed algorithm can separate moving objects more accurately in the scene with dynamic background.