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Artificial Intelligence Review

, Volume 49, Issue 3, pp 407–438 | Cite as

The effect of noise on foreground detection algorithms

  • Francisco Javier López-Rubio
  • Ezequiel López-Rubio
  • Miguel A. Molina-Cabello
  • Rafael Marcos Luque-Baena
  • Esteban J. Palomo
  • Enrique Domínguez
Article

Abstract

Background segmentation methods are exposed to the effects of different kinds of noise due to the limitations of image acquisition devices. This type of distortion can worsen the performance of segmentation methods because the input pixel values are altered. In this paper we study how several well-known background segmentation methods perform when the input is corrupted with several levels of uniform and Gaussian noise. Furthermore, few situations are reported where instead of an inconvenience, adding noise to the input may be desirable to attenuate some limitations of a method. In this work, the performance of nine well known methods is studied under both kinds of noise.

Keywords

Foreground detection Background modeling Uniform noise Gaussian noise Covariance matrix regularization 

Notes

Acknowledgments

This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2014-53465-R, project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Andalusia (Spain) under projects TIC-6213, project name Development of Self-Organizing Neural Networks for Information Technologies; and TIC-657, project name Self-organizing systems and robust estimators for video surveillance. Finally, it is partially supported by the Autonomous Government of Extremadura (Spain) under the project IB13113. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga and by the COMPUTAEX/CenitS center of the Autonomous Government of Extremadura.

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Computer Languages and Computer ScienceUniversity of MálagaMálagaSpain

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