Statistical unbiased background modeling for moving platforms

Michael Kirchhof, Uwe Stilla

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


Statistical background modeling is a standard technique for the detection of moving objects in a static scene. Nevertheless, the stateof-the-art approaches have several lacks for short sequences or quasistationary scenes. Quasi-static means that the ego-motion of the sensor is compensated by image processing. Our focus of attention goes back to the modeling of the pixel process, as it was introduced by Stauffer and Grimson. For quasi-stationary scenes the assignment of a pixel to an origin is uncertain. This assignment is an independent random process that contributes to the gray value. Since the typical update schemes are biased we introduce a novel update scheme based on the join mean and join variance of two independent distributions. The presented method can be seen as an update for the initial guess for more sophisticated algorithms that optimize the spatial distribution.

Original languageEnglish
Title of host publicationPhotogrammetric Image Analysis, ISPRS Conference, PIA 2011, Proceedings
Number of pages12
StatePublished - 2011
Externally publishedYes
EventISPRS Conference on Photogrammetric Image Analysis, PIA 2011 - Munich, Germany
Duration: 5 Oct 20117 Oct 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6952 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceISPRS Conference on Photogrammetric Image Analysis, PIA 2011


Dive into the research topics of 'Statistical unbiased background modeling for moving platforms'. Together they form a unique fingerprint.

Cite this