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arXiv · 1906.12064

Background Subtraction using Adaptive Singular Value Decomposition

Günther Reitberger·Tomas Sauer
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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.

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Günther Reitberger, Tomas Sauer. 2019-06-28. Background Subtraction using Adaptive Singular Value Decomposition. https://arxiv.org/abs/1906.12064

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