  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.
