Exponential H∞ stabilizing control of a class of uncertain impulsive switched systems
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Sparse tensor optimization has recently attracted much attention since it has many applications in areas such as biology, computer vision and information science. In this paper, we focus on the application of tensor optimization in surveillance video. Based on the static background of surveillance video, we introduce the new definition of rank-min-one tensor. Then we consider a rank-min-one and sparse tensor decomposition model for surveillance video. We establish the modified iterative reweighted l1algorithm (MIRL1), and give its convergence analysis. For synthetic and real surveillance data, numerical experiments are also presented to illustrate the efficiency of our proposed MIRL1.
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