This paper proposes an approximative 1-minimization algorithm with computationally efficient strategies to achieve\nreal-time performance of sparse model-based background subtraction. We use the conventional solutions of the\n1-minimization as a pre-processing step and convert the iterative optimization into simple linear addition and\nmultiplication operations. We then implement a novel background subtraction method that compares the\ndistribution of sparse coefficients between the current frame and the background model. The background model is\nformulated as a linear and sparse combination of atoms in a pre-learned dictionary. The influence of dynamic\nbackground diminishes after the process of sparse projection, which enhances the robustness of the implementation.\nThe results of qualitative and quantitative evaluations demonstrate the higher efficiency and effectiveness of the\nproposed approach compared with those of other competing methods.
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