Aggregated Dynamic Background Modeling

Amit Adam, Ehud Rivlin, Ilan Shimshoni

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Standard practices in background modeling learn a separate model for every pixel in the image. However, in dynamic scenes the connection between an observation and the place where it was observed is much less important and is usually random. For example, a wave observed in an ocean scene could easily have been observed at another place in the image. Moreover, during a limited learning period, we cannot expect to observe at every pixel all the possible background behaviors. We therefore develop in this paper a background model in which observations are decoupled from the place in the image where they were observed. A single non-parametric model is used to describe the dynamic region of the scene, aggregating the observations from the whole region. Using high-order features, we demonstrate the feasibility of our approach on challenging ocean scenes using only grayscale information.

Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
Pages3313 - 3316
Number of pages4
ISBN (Print)1424404819, 9781424404810
StatePublished - 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: 8 Oct 200611 Oct 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference2006 IEEE International Conference on Image Processing, ICIP 2006
Country/TerritoryUnited States
CityAtlanta, GA


  • Background modeling
  • Dynamic backgrounds
  • Video surveillance

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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