# Background Subtraction using Adaptive Singular Value Decomposition

arXiv: https://arxiv.org/abs/1906.12064 · HTML: https://mlsanity.com/paper/1906.12064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning](https://mlsanity.com/paper/2207.00089/) (2022)

## Abstract (arXiv)

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.