Efficient adaptive density estimation per image pixel for the task of background subtraction

Z. Zivkovic; Ferdinand van der Heijden

doi:10.1016/j.patrec.2005.11.005

Efficient adaptive density estimation per image pixel for the task of background subtraction

Z. Zivkovic
, Ferdinand van der Heijden

Research output: Contribution to journal › Article › Academic › peer-review

1456 Citations (Scopus)

174 Downloads (Pure)

Abstract

We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel. We also present a simple non-parametric adaptive density estimation method. The two methods are compared with each other and with some previously proposed algorithms.

Original language	Undefined
Pages (from-to)	773-780
Number of pages	8
Journal	Pattern recognition letters
Volume	27
Issue number	1636734 (P
DOIs	https://doi.org/10.1016/j.patrec.2005.11.005
Publication status	Published - 6 Jan 2006

Keywords

Background subtraction On-line density estimation Gaussian mixture model Non-parametric density estimation
IR-57717
METIS-237970
EWI-9308

Access to Document

10.1016/j.patrec.2005.11.005

http://eprints.eemcs.utwente.nl/secure2/9308/01/pattern_letters_2006.pdf

Cite this

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abstract = "We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel. We also present a simple non-parametric adaptive density estimation method. The two methods are compared with each other and with some previously proposed algorithms.",

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KW - METIS-237970

KW - EWI-9308

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