Background Subtraction via Fast Robust Matrix Completion

Behnaz Rezaei, Sarah Ostadabbas; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1871-1879


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.

Related Material

[pdf] [arXiv]
author = {Rezaei, Behnaz and Ostadabbas, Sarah},
title = {Background Subtraction via Fast Robust Matrix Completion},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}