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In the clutter environments, persistently tracking multiple objects is still a very challenging problem. In this paper, an A * algorithm with dynamic weights is developed to solve this problem. Firstly, the problem of multiple object tracking is considered as an integer programming of flow network model. Based on this model, afterward, we relax the problem of integer programming problem to a standard linear programming and achieve the global optimal solution with the proposed algorithm, which has been proven in this work. Compared with other advanced methods employed in the modern complex environments, the proposed method is on the merit of lower calculation complexity and better tracking accuracy and robustness. Finally, the simulation result is demonstrated, which reveals that the proposed algorithm saves the time costs vastly. [ABSTRACT FROM AUTHOR]

L 1 regularization and L p regularization are proposed for processing recovered images based on compressed sensing (CS). L 1 regularization can be solved as a convex optimization problem but is less sparse than L p (0 < p < 1). L p regularization is sparser than L 1 regularization but is more difficult to solve. This paper proposes joint L 1 / L p (0 < p < 1) regularization, which combines L p regularization and L 1 regularization. This joint regularization is applied to recover video of remote sensing based on CS. Joint regularization is sparser than L 1 regularization but is as easy to solve as L 1 regularization. A linearized Bregman reweighted iteration algorithm is proposed to solve the joint L 1 / L p regularization problem. The performance and capabilities of the linearized Bregman algorithm and linearized Bregman reweighted algorithm for solving the joint L 1 / L p regularization model are analyzed and compared through numerical simulations. [ABSTRACT FROM AUTHOR]