  A compressive sensing method combined with decomposition of a matrix formed
with image frames of a surveillance video into low rank and sparse matrices is
proposed to segment the background and extract moving objects in a surveillance
video. The video is acquired by compressive measurements, and the measurements
are used to reconstruct the video by a low rank and sparse decomposition of
matrix. The low rank component represents the background, and the sparse
component is used to identify moving objects in the surveillance video. The
decomposition is performed by an augmented Lagrangian alternating direction
method. Experiments are carried out to demonstrate that moving objects can be
reliably extracted with a small amount of measurements.
