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

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.

Keywords

• robust principal component analysis
• video separation
• compressive measurements
• prior information
• optical flow
• motion estimation
• motion compensation