# Background Subtraction using Adaptive Singular Value Decomposition

## TL;DR

This paper introduces an efficient adaptive singular value decomposition method for background subtraction in sensor data, enabling robust and fast differentiation between relevant and irrelevant information in video frames.

## Contribution

It presents a novel iterative SVD approach that maintains and updates a background model efficiently, improving background subtraction performance.

## Key findings

- Achieves state-of-the-art background subtraction results.
- Provides a computationally efficient and robust adaptive SVD algorithm.
- Demonstrates effectiveness through qualitative and quantitative evaluations.

## 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.