  Although it has been widely discussed in video surveillance, background
subtraction is still an open problem in the context of complex scenarios, e.g.,
dynamic backgrounds, illumination variations, and indistinct foreground
objects. To address these challenges, we propose an effective background
subtraction method by learning and maintaining an array of dynamic texture
models within the spatio-temporal representations. At any location of the
scene, we extract a sequence of regular video bricks, i.e. video volumes
spanning over both spatial and temporal domain. The background modeling is thus
posed as pursuing subspaces within the video bricks while adapting the scene
variations. For each sequence of video bricks, we pursue the subspace by
employing the ARMA (Auto Regressive Moving Average) Model that jointly
characterizes the appearance consistency and temporal coherence of the
observations. During online processing, we incrementally update the subspaces
to cope with disturbances from foreground objects and scene changes. In the
experiments, we validate the proposed method in several complex scenarios, and
show superior performances over other state-of-the-art approaches of background
subtraction. The empirical studies of parameter setting and component analysis
are presented as well.
