Advanced

Background segmentation beyond RGB

Kristensen, Fredrik LU ; Nilsson, Peter LU and Öwall, Viktor LU (2006) 7th Asian Conference on Computer Vision (ACCV’06), 2006 3852. p.602-612
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Computer Vision – ACCV 2006 / Lecture Notes in Computer Science
editor
S, Narayanan and
volume
3852
pages
602 - 612
publisher
Springer
conference name
7th Asian Conference on Computer Vision (ACCV’06), 2006
conference location
Hyderabad, India
conference dates
2006-01-13
external identifiers
  • wos:000235773200060
  • scopus:33744935019
ISSN
0302-9743
1611-3349
ISBN
978-3-540-31244-4
DOI
10.1007/11612704_60
language
English
LU publication?
yes
id
730e04f2-6143-4f21-a795-af63d3c883bf (old id 415784)
date added to LUP
2007-10-08 08:30:18
date last changed
2019-07-16 01:39:55
@inproceedings{730e04f2-6143-4f21-a795-af63d3c883bf,
  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.},
  author       = {Kristensen, Fredrik and Nilsson, Peter and Öwall, Viktor},
  editor       = {S, Narayanan},
  isbn         = {978-3-540-31244-4},
  issn         = {0302-9743},
  language     = {eng},
  location     = {Hyderabad, India},
  pages        = {602--612},
  publisher    = {Springer},
  title        = {Background segmentation beyond RGB},
  url          = {http://dx.doi.org/10.1007/11612704_60},
  volume       = {3852},
  year         = {2006},
}