Background Subtraction via Fast Robust Matrix Completion

TL;DR

This paper introduces a fast robust matrix completion method for background subtraction in videos, offering computational efficiency and comparable or better accuracy than existing methods, suitable for high-resolution multi-channel video analysis.

Contribution

The paper proposes a novel fast robust matrix completion approach using in-face extended Frank-Wolfe algorithm for efficient background modeling in video analysis.

Findings

Faster computation than existing RPCA and RMC methods, at least twice as quick.

Achieved comparable or improved accuracy in background subtraction tasks.

Validated on BMC and SABS datasets with promising results.

Abstract

Background subtraction is the primary task of the majority of video inspection systems. The most important part of the background subtraction which is common among different algorithms is background modeling. In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel videos. Our model is based on the assumption that background in natural images lies on a low-dimensional subspace. We formulated and solved this problem in a low-rank matrix completion framework. In modeling the background, we benefited from the in-face extended Frank-Wolfe algorithm for solving a defined convex optimization problem. We evaluated our fast robust matrix completion (fRMC) method on both background models challenge (BMC) and Stuttgart artificial background subtraction (SABS) datasets. The results were compared with the robust principle component analysis (RPCA) and low-rank robust matrix completion (RMC) methods, both solved by inexact augmented Lagrangian multiplier (IALM). The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.