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Background segmentation beyond RGB

In English

Författare

Redaktör

  • Narayanan S

Summary, in English

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.

Publiceringsår

2006

Språk

Engelska

Sidor

602-612

Publikation/Tidskrift/Serie

Computer Vision – ACCV 2006 / Lecture Notes in Computer Science

Volym

3852

Dokumenttyp

Konferensbidrag

Förlag

Springer

Ämne

  • Electrical Engineering, Electronic Engineering, Information Engineering

Conference name

7th Asian Conference on Computer Vision (ACCV’06), 2006

Conference date

2006-01-13

Conference place

Hyderabad, India

Status

Published

Forskningsgrupp

  • Elektronikkonstruktion

ISBN/ISSN/Övrigt

  • ISSN: 0302-9743
  • ISSN: 1611-3349
  • ISBN: 978-3-540-31244-4