Neural Computing and Applications

, Volume 19, Issue 2, pp 179–186 | Cite as

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

  • Lucia Maddalena
  • Alfredo PetrosinoEmail author
KES 2008


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.


Moving object detection Background subtraction Multivalued background modeling Self-organization Neural network Spatial coherence 


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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.ICAR, National Research CouncilNaplesItaly
  2. 2.DSA, University of Naples ParthenopeNaplesItaly

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