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Applied Intelligence

, Volume 49, Issue 5, pp 1771–1784 | Cite as

Challenging situations for background subtraction algorithms

  • Silvio R. R. SanchesEmail author
  • Claiton Oliveira
  • Antonio C. Sementille
  • Valdinei Freire
Article
  • 189 Downloads

Abstract

Background subtraction is the prerequisite for a wide range of applications including video surveillance, smart environments and content retrieval. Real environments present some challenging situations even for the most recent algorithms, such as shadows, illumination changes, dynamic background, among others. If a real environment is previously known and the challenging situations of this environment can be predicted, the choice of an appropriate algorithm to deal with such situations may be essential for obtaining better segmentation results. In our work, we identify the main situations that affect the performance of background subtraction algorithms and present a classification of these challenging situations. In addition, we present a solution that uses videos and ground-truths from existing datasets to evaluate the performance of segmentation algorithms when they need to deal with a specific challenging situation.

Keywords

Background subtraction Foreground extraction Algorithm evaluation Challenging situation 

Notes

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidade Tecnológica Federal do ParanáCornélio ProcópioBrazil
  2. 2.Universidade Estadual Paulista “Júlio de Mesquita Filho”BauruBrazil
  3. 3.Universidade de São PauloSão PauloBrazil

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