ROBUST SUBSPACE RECOVERY VIA DUAL SPARSITY PURSUIT

摘要

A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L−1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L−1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.

主权项

1. A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework, the method comprising:
creating an image matrix based on a continuous image sequence; initializing a background matrix, a foreground matrix, and a coefficient matrix; performing a subspace recovery process over a plurality of iterations, the subspace recovery process comprising:
update the background matrix based on the image matrix and the foreground matrix,minimize an L−1 norm of the coefficient matrix using a first linearized soft-thresholding process, andminimize an L−1 norm of the foreground matrix using a second linearized soft-thresholding process; generating one or more background images based on the background matrix; and generating one or more foreground images based on the foreground matrix.