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Towards Benchmarking Scene Background Initialization

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9281)

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.

Keywords

  • Background initialization
  • Video analysis
  • Video surveillance

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Correspondence to Lucia Maddalena .

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Maddalena, L., Petrosino, A. (2015). Towards Benchmarking Scene Background Initialization. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_57

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  • DOI: https://doi.org/10.1007/978-3-319-23222-5_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23221-8

  • Online ISBN: 978-3-319-23222-5

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