Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4970111 | Pattern Recognition Letters | 2017 | 12 Pages |
Abstract
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. Limited attention to the problem is given in the literature on background modeling, that mainly regards model representation and updating. Therefore, we propose a taxonomy study for background initialization, providing the basis for a fair and easy comparison of existing and future methods, on a common dataset of groundtruthed sequences, with a common set of metrics, and based on reproducible results. Experimental results highlight the most promising approaches as well as main open issues for background initialization.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Thierry Bouwmans, Lucia Maddalena, Alfredo Petrosino,