Bacteria-filters: Persistent particle filters for background subtraction

Yair Movshovitz-Attias*, Shmuel Peleg

*Corresponding author for this work

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

2 Scopus citations

Abstract

Moving objects are usually detected by measuring the appearance change from a background model. The background model should adapt to slow changes such as illumination, but detect faster changes caused by moving objects. Particle filters do an excellent task in modeling non parametric distributions as needed for a background model, but may adapt too quickly to the foreground objects. A persistent particle filter is proposed, following bacterial persistence. Bacterial persistence is linked to the random switch of bacteria between two states: A normal growing cell and a dormant but persistent cell. The dormant cells can survive stress such as antibiotics. When a dormant cell switches to a normal status after the stress is over, bacterial growth continues. Similar to bacteria, particles will switch between dormant and active states, where dormant particles will not adapt to the changing environment. A further modification of particle filters allows discontinuous jumps into new parameters enabling foreground objects to join the background when they stop moving. This can also quickly build multi-modal distributions.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages677-680
Number of pages4
DOIs
StatePublished - 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010

Publication series

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

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong
Period26/09/1029/09/10

Keywords

  • Background model
  • Object detection and tracking
  • Particle filter
  • Tracking filters

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