Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation

TL;DR

This paper introduces a novel compressive online RPCA method utilizing optical flow for improved real-time video foreground-background separation from limited measurements, outperforming existing approaches.

Contribution

It presents a new online RPCA framework that incorporates optical flow and prior information to enhance foreground-background separation from compressive measurements.

Findings

Outperforms existing methods in visual and quantitative metrics

Effectively separates foreground and background from limited measurements

Utilizes optical flow to improve foreground prior estimation

Abstract

In the context of online Robust Principle Component Analysis (RPCA) for the video foreground-background separation, we propose a compressive online RPCA with optical flow that separates recursively a sequence of frames into sparse (foreground) and low-rank (background) components. Our method considers a small set of measurements taken per data vector (frame), which is different from conventional batch RPCA, processing all the data directly. The proposed method also incorporates multiple prior information, namely previous foreground and background frames, to improve the separation and then updates the prior information for the next frame. Moreover, the foreground prior frames are improved by estimating motions between the previous foreground frames using optical flow and compensating the motions to achieve higher quality foreground prior. The proposed method is applied to online video foreground and background separation from compressive measurements. The visual and quantitative results show that our method outperforms the existing methods.