Robust low rank and sparse decomposition from blurred video frames
AbstractRecently, the low-rank and sparse decomposition
problem has attracted attention in several applications, especially
surveillance videos. Due to the physical limitations in acquisition
systems, measured frames are blurred by a low-pass filter.
In this article, we aim to decompose blurred videos' frames
into low-rank and sparse components, in order to extract the
background. Unlike conventional methods, we simultaneously take into
account the blurring effect, as well as the missing data. Our simulation
results confirmed the advantage of this approach in extracting low-rank
components in surveillance videos.