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Scene background initialization: A taxonomy
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DOI：10.1016/j.patrec.2016.12.024.png)
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Abstract
En 中文
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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Background initialization
Background generation
Bootstrapping
Background estimation
Background reconstruction
Background recovery
Initial background extraction
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