Towards Benchmarking Scene Background Initialization

TL;DR

This paper introduces a benchmark dataset and evaluation framework for scene background initialization methods, facilitating fair comparison and progress in the field.

Contribution

It provides a publicly available dataset, ground truths, and metrics for evaluating background initialization methods, addressing the lack of standard benchmarks.

Findings

Compared existing methods using the new benchmark

Identified strengths and weaknesses of different approaches

Provided a baseline for future research in background initialization

Abstract

Given a set of images of a scene taken at different times, the availability of an initial background model that describes the scene without foreground objects is the prerequisite for a wide range of applications, ranging from video surveillance to computational photography. Even though several methods have been proposed for scene background initialization, the lack of a common groundtruthed dataset and of a common set of metrics makes it difficult to compare their performance. To move first steps towards an easy and fair comparison of these methods, we assembled a dataset of sequences frequently adopted for background initialization, selected or created ground truths for quantitative evaluation through a selected suite of metrics, and compared results obtained by some existing methods, making all the material publicly available.