Adaptive low rank and sparse decomposition of video using compressive sensing
This work addresses video analysis for surveillance applications, presenting an incremental improvement over existing methods.
The paper tackles video reconstruction and analysis for surveillance using compressive sensing by developing an adaptive low rank and sparse decomposition method that integrates background subtraction into reconstruction, resulting in low latency and improved robustness compared to prior methods.
We address the problem of reconstructing and analyzing surveillance videos using compressive sensing. We develop a new method that performs video reconstruction by low rank and sparse decomposition adaptively. Background subtraction becomes part of the reconstruction. In our method, a background model is used in which the background is learned adaptively as the compressive measurements are processed. The adaptive method has low latency, and is more robust than previous methods. We will present experimental results to demonstrate the advantages of the proposed method.