Data Separation of L1-minimization for Real-time Motion Detection

The `1-minimization used to seek the sparse solution restricts the applicability of compressed sensing. This paper proposes a data separation algorithm with computationally efficient strategies to achieve real-time performance of sparse model based motion detection. We use the traditional pursuit algorithms as a pre-process step that converts the iterative optimization into linear addition and multiplication operations. A novel motion detection method is implemented to compare the difference between the current frame and the background model in terms of sparse coefficients. The influence of dynamic texture or statistical noise diminishes after the process of sparse projection; thus, enhancing the robustness of the implementation. Results of the qualitative and quantitative evaluations demonstrate the higher efficiency and effectiveness of the proposed approach compared with those of other competing methods.