Non-parametric background and shadow modeling for object detection

Tatsuya Tanaka, Atsushi Shimada, Daisaku Arita, Rin Ichiro Taniguchi

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

23 Citations (Scopus)


We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Number of pages10
EditionPART 1
ISBN (Print)9783540763857
Publication statusPublished - 2007
Event8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, Japan
Duration: Nov 18 2007Nov 22 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4843 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other8th Asian Conference on Computer Vision, ACCV 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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