Probabilistic model-based background subtraction
Krüger, V. LU
; Anderson, J.
and Prehn, T.
(2005)
13th International Conference on Image Analysis and Processing, ICIAP 2005
In Lecture Notes in Computer Science
3617.
p.180-187
- Abstract
In this paper we introduce a model-based background subtraction approach where first silhouettes, which model the correlations between neightboring pixels are being learned and where then Bayesian propagation over time is used to select the proper silhouette model and tracking parameters. Bayes propagation is attractive in our application as it allows to deal with uncertainties in the video data during tracking. We eploy a particle filter for density estimation. We have extensively tested our approach on suitable outdoor video data.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/529e91d2-a545-4b57-bee7-58979a9803fd
- author
- Krüger, V.
LU
; Anderson, J.
and Prehn, T.
- publishing date
- 2005-12-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Image Analysis and Processing - ICIAP 2005, 13th International Conference, Proceedings
- series title
- Lecture Notes in Computer Science
- volume
- 3617
- pages
- 8 pages
- conference name
- 13th International Conference on Image Analysis and Processing, ICIAP 2005
- conference location
- Cagliari, Italy
- conference dates
- 2005-09-06 - 2005-09-08
- external identifiers
-
- scopus:33745144341
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 3540288694
- 9783540288695
- DOI
- 10.1007/11553595_22
- language
- English
- LU publication?
- no
- id
- 529e91d2-a545-4b57-bee7-58979a9803fd
- date added to LUP
- 2019-07-08 21:19:48
- date last changed
- 2024-01-01 15:52:45
@inproceedings{529e91d2-a545-4b57-bee7-58979a9803fd,
abstract = {{<p>In this paper we introduce a model-based background subtraction approach where first silhouettes, which model the correlations between neightboring pixels are being learned and where then Bayesian propagation over time is used to select the proper silhouette model and tracking parameters. Bayes propagation is attractive in our application as it allows to deal with uncertainties in the video data during tracking. We eploy a particle filter for density estimation. We have extensively tested our approach on suitable outdoor video data.</p>}},
author = {{Krüger, V. and Anderson, J. and Prehn, T.}},
booktitle = {{Image Analysis and Processing - ICIAP 2005, 13th International Conference, Proceedings}},
isbn = {{3540288694}},
issn = {{1611-3349}},
language = {{eng}},
month = {{12}},
pages = {{180--187}},
series = {{Lecture Notes in Computer Science}},
title = {{Probabilistic model-based background subtraction}},
url = {{http://dx.doi.org/10.1007/11553595_22}},
doi = {{10.1007/11553595_22}},
volume = {{3617}},
year = {{2005}},
}