  A basic algorithmic task in automated video surveillance is to separate
background and foreground objects. Camera tampering, noisy videos, low frame
rate, etc., pose difficulties in solving the problem. A general approach that
classifies the tampered frames, and performs subsequent analysis on the
remaining frames after discarding the tampered ones, results in loss of
information. Several robust methods based on robust principal component
analysis (PCA) have been introduced to solve this problem. To date,
considerable effort has been expended to develop robust PCA via Principal
Component Pursuit (PCP) methods with reduced computational cost and visually
appealing foreground detection. However, the convex optimizations used in these
algorithms do not scale well to real-world large datasets due to large matrix
inversion steps. Also, an integral component of these foreground detection
algorithms is singular value decomposition which is nonrobust. In this paper,
we present a new video surveillance background modelling algorithm based on a
new robust singular value decomposition technique rSVDdpd which takes care of
both these issues. We also demonstrate the superiority of our proposed
algorithm on a benchmark dataset and a new real-life video surveillance dataset
in the presence of camera tampering. Software codes and additional
illustrations are made available at the accompanying website rSVDdpd Homepage
(https://subroy13.github.io/rsvddpd-home/)
