Background Subtraction using Adaptive Singular Value Decomposition
Abstract
An important task when processing sensor data is to distinguish relevant from irrelevant data. This paper describes a method for an iterative singular value decomposition that maintains a model of the background via singular vectors spanning a subspace of the image space, thus providing a way to determine the amount of new information contained in an incoming frame. We update the singular vectors spanning the background space in a computationally efficient manner and provide the ability to perform block-wise updates, leading to a fast and robust adaptive SVD computation. The effects of those two properties and the success of the overall method to perform a state of the art background subtraction are shown in both qualitative and quantitative evaluations.
Cite
@article{arxiv.1906.12064,
title = {Background Subtraction using Adaptive Singular Value Decomposition},
author = {Günther Reitberger and Tomas Sauer},
journal= {arXiv preprint arXiv:1906.12064},
year = {2019}
}