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A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection

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Abstract

The detection of moving objects from stationary cameras is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously proposed approach to background subtraction based on self-organization through artificial neural networks, that has been shown to well cope with several of the well known issues for background maintenance. Here, we propose a spatial coherence variant to such approach to enhance robustness against false detections and formulate a fuzzy model to deal with decision problems typically arising when crisp settings are involved. We show through experimental results and comparisons that higher accuracy values can be reached for color video sequences that represent typical situations critical for moving object detection.

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Authors and Affiliations

  1. ICAR, National Research Council, Via P. Castellino 111, 80131, Naples, Italy

    Lucia Maddalena

  2. DSA, University of Naples Parthenope, Centro Direzionale, Isola C/4, 80143, Naples, Italy

    Alfredo Petrosino

Authors
  1. Lucia Maddalena
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  2. Alfredo Petrosino
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Correspondence to Alfredo Petrosino.

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Maddalena, L., Petrosino, A. A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection. Neural Comput & Applic 19, 179–186 (2010). https://doi.org/10.1007/s00521-009-0285-8

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  • DOI: https://doi.org/10.1007/s00521-009-0285-8

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