Background segmentation beyond RGB
(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:
https://lup.lub.lu.se/record/415784
- author
- Kristensen, Fredrik LU ; Nilsson, Peter LU and Öwall, Viktor LU
- organization
- publishing date
- 2006
- 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
- 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
- 1611-3349
- 0302-9743
- 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
- 2016-04-01 12:14:49
- date last changed
- 2024-12-04 08:21:41
@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}}, booktitle = {{Computer Vision – ACCV 2006 / Lecture Notes in Computer Science}}, editor = {{S, Narayanan}}, isbn = {{978-3-540-31244-4}}, issn = {{1611-3349}}, language = {{eng}}, pages = {{602--612}}, publisher = {{Springer}}, title = {{Background segmentation beyond RGB}}, url = {{http://dx.doi.org/10.1007/11612704_60}}, doi = {{10.1007/11612704_60}}, volume = {{3852}}, year = {{2006}}, }