Moving target segmentation method based on tensor decomposition and graph Laplacian regularization

A new method for segmenting the target foreground from image frames is proposed by utilizing the theory of graph signal processing and the tensor decomposition model aiming at the problem that the segmentation results of the existing foreground segmentation methods in image frames under dynamic scenes are not high in accuracy. The intrinsic connection between image pixels in each frame of an image sequence is modeled as a graph, the image pixel intensities are modeled as graph signals, and the correlation between pixels is characterized by the graph model. According to the significant difference between the dynamic background and the target change in the moving foreground in the image sequence, the dynamic background region in each image frame is smoothed and suppressed, and the disturbing information of the dynamic background is transformed into the useful component information in the low-rank subspace. The connectivity between image pixels can be characterized by the graph Laplacian regularization term, and then the target foreground segmentation problem in the image sequence is equivalent to a constrained optimization problem with tensor decomposition and graph Laplacian regularization term. The alternating direction multiplier method is used to solve the optimization problem, and the simulation results on real scene data set verify the effectiveness of the algorithm.