Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background
  Subtraction
Basit Alawode Sajid Javed
Video background subtraction is one of the fundamental problems in computer
vision that aims to segment all moving objects. Robust principal component
analysis has been identified as a promising unsupervised paradigm for
background subtraction tasks in the last decade thanks to its competitive
performance in a number of benchmark datasets. Tensor robust principal
component analysis variations have improved background subtraction performance
further. However, because moving object pixels in the sparse component are
treated independently and do not have to adhere to spatial-temporal
structured-sparsity constraints, performance is reduced for sequences with
dynamic backgrounds, camouflaged, and camera jitter problems. In this work, we
present a spatial-temporal regularized tensor sparse RPCA algorithm for precise
background subtraction. Within the sparse component, we impose spatial-temporal
regularizations in the form of normalized graph-Laplacian matrices. To do this,
we build two graphs, one across the input tensor spatial locations and the
other across its frontal slices in the time domain. While maximizing the
objective function, we compel the tensor sparse component to serve as the
spatiotemporal eigenvectors of the graph-Laplacian matrices. The disconnected
moving object pixels in the sparse component are preserved by the proposed
graph-based regularizations since they both comprise of spatiotemporal
subspace-based structure. Additionally, we propose a unique objective function
that employs batch and online-based optimization methods to jointly maximize
the background-foreground and spatial-temporal regularization components.
Experiments are performed on six publicly available background subtraction
datasets that demonstrate the superior performance of the proposed algorithm
compared to several existing methods. Our source code will be available very
soon.