Spatiotemporal algorithm for background subtraction

S. Derin Babacan*, Thrasyvoulos N Pappas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Background modeling and subtraction is a fundamental task in many computer vision and video processing applications. We present a novel probabilistic background modeling and subtraction method that exploits spatial and temporal dependencies between pixels. By using an initial clustering of the background scene, we model each pixel by a mixture of spatiotemporal Gaussian distributions, where each distribution represents locally a region in the neighborhood of the pixel. By extracting the local properties around each pixel, the proposed method obtains accurate models of dynamic backgrounds that are highly effective in detecting foreground objects. Experimental results for indoor and outdoor surveillance videos in comparison with other multimodal methods demonstrate the performance advantages of the proposed method.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
DOIs
StatePublished - Aug 6 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Background subtraction
  • Bayesian formulation
  • Object detection
  • Probabilistic model
  • Video processing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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