Use of fast algorithm for adaptive background modeling with Parzen density estimation to detect objects

Tatsuya Tanaka, Atsushi Shimada, Daisaku Arita, Rin-Ichiro Taniguchi, Yoichi Tomiura

Research output: Contribution to journalArticle

Abstract

We propose the use of a fast algorithm for estimating background models. This algorithm makes use of Parzen density estimation in non-stationary scenes. Each pixel has a probability density function this is used to approximate the value of pixels observed in a video sequence. Estimating this function quickly and accurately is important. In our approach, the probability density function is partially updated within the range of a window function based on the value observed. The model quickly adapts to changes in the scene and foreground objects can be robustly detected. Several experiments show the effectiveness of our approach.

Original languageEnglish
Pages (from-to)2045-2052
Number of pages8
JournalKyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers
Volume62
Issue number12
DOIs
Publication statusPublished - Jan 1 2008

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Probability density function
Pixels
Experiments

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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