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Real-time scene background initialization based on spatio-temporal neighborhood exploration

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Abstract

In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods.

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Notes

  1. For comparison with RMR, we used the software available at: http://www-vpu.eps.uam.es/publications/BE_RMR/

  2. http://sbmi2015.na.icar.cnr.it/SBIdataset.html

  3. http://pione.dinf.usherbrooke.ca/method/178/

  4. http://opencv.org/

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Authors and Affiliations

  1. SERCOM, Ecole Polytechnique de Tunisie, Université de Carthage, B.P.743, 2078, La Marsa, Tunisia

    Wided Souidene Mseddi, Marwa Jmal & Rabah Attia

  2. L2TI, Institut Galilée, Université Paris 13, 99, Avenue Jean-Baptiste Clement, 93430, Villetaneuse, France

    Wided Souidene Mseddi

  3. Telnet Innovation Labs, Telnet Holding, Ariana, Tunisia

    Marwa Jmal

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  1. Wided Souidene Mseddi
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  2. Marwa Jmal
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Correspondence to Wided Souidene Mseddi.

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Mseddi, W.S., Jmal, M. & Attia, R. Real-time scene background initialization based on spatio-temporal neighborhood exploration. Multimed Tools Appl 78, 7289–7319 (2019). https://doi.org/10.1007/s11042-018-6399-1

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  • DOI: https://doi.org/10.1007/s11042-018-6399-1

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