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.
Cite
@article{arxiv.1506.04051,
title = {Towards Benchmarking Scene Background Initialization},
author = {Lucia Maddalena and Alfredo Petrosino},
journal= {arXiv preprint arXiv:1506.04051},
year = {2015}
}