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Improved Gaussian Mixture Models for Adaptive Foreground Segmentation

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Abstract

Adaptive foreground segmentation is traditionally performed using Stauffer and Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated outdoors sequence.

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Authors and Affiliations

  1. Center for TeleInFrastruktur, Aalborg University, Fredrik Bajers Vej 7A, 9220, Aalborg, Denmark

    Nikolaos Katsarakis, Zheng-Hua Tan & Ramjee Prasad

  2. Athens Information Technology, 0.8 km Markopoulou Ave., 19002, Peania, Athens, Greece

    Aristodemos Pnevmatikakis

Authors
  1. Nikolaos Katsarakis
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  2. Aristodemos Pnevmatikakis
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  3. Zheng-Hua Tan
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  4. Ramjee Prasad
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Corresponding author

Correspondence to Aristodemos Pnevmatikakis.

Additional information

Part of this work has been carried out in the scope of the EC co-funded projects SMART (FP7-287583) and eWALL (FP7-610658).

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Katsarakis, N., Pnevmatikakis, A., Tan, ZH. et al. Improved Gaussian Mixture Models for Adaptive Foreground Segmentation. Wireless Pers Commun 87, 629–643 (2016). https://doi.org/10.1007/s11277-015-2628-3

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  • DOI: https://doi.org/10.1007/s11277-015-2628-3

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