Background segmentation beyond RGB

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

To efficiently classify and track video objects in a surveillance application, it is essential to reduce the amount of streaming data. One solution is to segment the video into background, i.e. stationary objects, and foreground, i.e. moving objects, and then discard the background. One such motion segmentation algorithm that has proven reliable is the Stauffer and Crimson algorithm. This paper investigates how different color spaces affect the segmentation result in terms of noise and shadow sensitivity. Shadows are especially problematic since they not only distort shape but can also result in falsely connected objects that will complicate tracking and classification. Therefore, a new decision kernel for the segmentation algorithm is presented. This kernel alters the probability of foreground detection to reduce shadows and to increase the chance of correct segmentation for objects with a skin tone color, e.g. faces.

Details

Authors
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Electrical Engineering, Electronic Engineering, Information Engineering
Original languageEnglish
Title of host publicationComputer Vision – ACCV 2006 / Lecture Notes in Computer Science
EditorsNarayanan S
PublisherSpringer
Pages602-612
Volume3852
ISBN (Print)978-3-540-31244-4
Publication statusPublished - 2006
Publication categoryResearch
Peer-reviewedYes
Event7th Asian Conference on Computer Vision (ACCV’06), 2006 - Hyderabad, India
Duration: 2006 Jan 13 → …
Conference number: 7

Publication series

Name
Volume3852
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th Asian Conference on Computer Vision (ACCV’06), 2006
CountryIndia
CityHyderabad
Period2006/01/13 → …