Moving Object Detection Method Based on Low-Rank and Sparse Decomposition in Dynamic Background

Focusing on the issue that the detection accuracy of moving object is significantly reduced by background motion, a low-rank and sparse decomposition based moving object detection method is developed. Firstly, in order to solve the problem that the nuclear norm over-penalizing large singular values lead to the optimal solution of the obtained minimization problem can not be obtained and then the detection performance is decreased, the gamma norm ($\gamma {\rm{ - norm}}$) is introduced to acquire almost unbiased approximation of rank function. In what follows, the ${L_{{1 / 2}}}$ norm is used to extract the sparse foreground object to enhance the robustness to noise, and the spatial continuity constraint is proposed to suppress dynamic background pixels such that the moving object detection model can be constructed on the basis of the sparse and spatially discontinuous nature of the false alarm pixels. After that, the Augmented Lagrange Multiplier (ALM) method, which is the extension of the Alternating Direction Minimizing (ADM) strategy, can be employed to deal with the acquired constrained minimization problem. Compared with some state-of-the-art algorithms, the experimental results show that the proposed method can significantly improve the accuracy of moving object detection in the case of dynamic background.