Video foreground-background separation based on generalized nonconvex robust principal component analysis

Aiming at the poor precision problem of video foreground-background separation based on traditional robust principal component analysis, this paper proposes a new model called generalized nonconvex robust principal component analysis (GNRPCA) model. This model adopts the generalized nuclear norm and generalized norm to replace the rank function and l0 norm in the robust principal component analysis model, respectively, which can solve the problems of the excessive penalty for the surrogate functions of the rank function and sparsity function existing in traditional robust principal component analysis model, and leading to poor approaching degree. Then, the alternating direction method of multiplier (ADMM) is adopted to solve the proposed GNRPCA model. Finally, the proposed algorithm was used for video foreground-background separation. Simulation experiments were conducted, the experiment results were analyzed. The experiment results prove that the average F-measure value of the proposed algorithm is 0.589 2, which is 13% higher than the truncated nuclear norm algorithm. And the proposed algorithm is more superior and effective than other video foreground-background separation algorithms based on robust principal component analysis. © 2020, Science Press. All right reserved.