Adaptive low rank and sparse decomposition of video using compressive sensing

TL;DR

This paper introduces an adaptive low rank and sparse decomposition method for reconstructing and analyzing surveillance videos using compressive sensing, integrating background subtraction into the reconstruction process.

Contribution

It presents a novel adaptive approach that jointly reconstructs videos and performs background subtraction efficiently from compressive measurements.

Findings

The method achieves low latency video reconstruction.

It demonstrates robustness over previous methods.

Experimental results validate the advantages of the approach.

Abstract

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.