Compressive online robust principal component analysis with multiple prior information

Abstract

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Compressive Online Video Background-Foreground Separation Using Multiple Prior Information and Optical Flow

“…In this paper, we propose a compressive online robust PCA with optical flow (CORPCA-OF 1 ) method, which is based on our previous work in [8]. We leverage information from previously separated foreground frames via optical flow [6].…”

“…We leverage information from previously separated foreground frames via optical flow [6]. The novelty of CORPCA-OF over CORPCA [8] is that we make use of optical flow to estimate and compensate motions between the foreground frames, in order to generate new prior foreground frames. These new prior frames have high correlation with the current frame and thus improve the separation.…”

“…We provide a brief introduction to the CORPCA algorithm [8], on which our proposed CORPCA-OF is built upon. Based on this, we formulate the problem statement in Sec.…”

“…The recent work in [8] leverages correlations across the previously separated foreground frames. However, displacements between the previous foreground frames and the current frame are not taken into account.…”

“…In the next instance t, we have access to compressive measurements of the full frame, a.k.a., vector x t + v t , that is, we observe y t = Φ(x t + v t ), where Φ ∈ R m×n (m < n) is a random projection. The recovery problem at time instance t is thus written [8] as…”