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Background segmentation beyond RGB

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Author

Editor

  • 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.

Publishing year

2006

Language

English

Pages

602-612

Publication/Series

Computer Vision – ACCV 2006 / Lecture Notes in Computer Science

Volume

3852

Document type

Conference paper

Publisher

Springer

Topic

  • 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

Research group

  • Elektronikkonstruktion

ISBN/ISSN/Other

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