  The objective of this study is to address the problem of
background/foreground separation with missing pixels by combining the video
acquisition, video recovery, background/foreground separation into a single
framework. To achieve this, a smooth robust tensor completion (SRTC) model is
proposed to recover the data and decompose it into the static background and
smooth foreground, respectively. Specifically, the static background is modeled
by the low-rank tucker decomposition and the smooth foreground (moving objects)
is modeled by the spatiotemporal continuity, which is enforced by the total
variation regularization. An efficient algorithm based on tensor proximal
alternating minimization (tenPAM) is implemented to solve the proposed model
with global convergence guarantee under very mild conditions. Extensive
experiments on real data demonstrate that the proposed method significantly
outperforms the state-of-the-art approaches for background/foreground
separation with missing pixels.
