Abstract
In the context of video background–foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method operates on a small set of measurements taken per frame, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-ℓ1 minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background–foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.
Highlights
Emerging applications in surveillance and autonomous driving are challenging the existing visual systems to detect and understand objects from visual observations
The CORPCA-OF method was compared with CORPCA against Escalator and Fountain sequences for compressive measurements
It can be seen that the background and foreground frames of CORPCA-OF for both Bootstrap and Curtain are much smoother and the contents have better structure compared to that of CORPCA
Summary
Emerging applications in surveillance and autonomous driving are challenging the existing visual systems to detect and understand objects from visual observations. Video background–foreground separation is one of most important components for object detection, identification, and tracking. A video sequence can be separated into a slowly changing background (modeled by L as a low-rank component) and the foreground (modeled by S, which is a sparse component). RPCA [1,2] was shown to be a robust method for separating the low-rank and sparse components. RPCA decomposes a data matrix M into the sum of unknown sparse S and low-rank L by solving the Principal Component Pursuit (PCP) [1] problem: min k Lk∗ + λkSk1 subject to M = L + S, L,S (1). Motion compensated prior foreground frames x't-3 x't-2 x't-1 xt-2 xt-1. Prior foreground frames xt-3 xt + vt. Foreground xt Foreground xt and background vt frames used as prior information for the frame
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