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Gradient-enhanced particle filter for vision-based motion capture

Grest, Daniel and Krüger, Volker LU orcid (2007) 2nd Workshop on Human Motion Understanding, Modeling, Capture and Animation In Lecture Notes in Computer Science 4814. p.28-41
Abstract

Tracking of rigid and articulated objects is usually addressed within a particle filter framework or by correspondence based gradient descent methods. We combine both methods, such that (a) the correspondence based estimation gains the advantage of the particle filter and becomes able to follow multiple hypotheses while (b) the particle filter becomes able to propagate the particles in a better manner and thus gets by with a smaller number of particles. Results on noisy synthetic depth data show that the new method is able to track motion correctly where the correspondence based method fails. Further experiments with real-world stereo data underline the advantages of our coupled method.

Please use this url to cite or link to this publication:
author
and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings
series title
Lecture Notes in Computer Science
volume
4814
pages
14 pages
publisher
Springer
conference name
2nd Workshop on Human Motion Understanding, Modeling, Capture and Animation
conference location
Rio de Janeiro, Brazil
conference dates
2007-10-20 - 2007-10-20
external identifiers
  • scopus:38149119843
ISSN
0302-9743
1611-3349
ISBN
9783540757023
DOI
10.1007/978-3-540-75703-0_3
language
English
LU publication?
no
id
aab1d899-c404-40d8-aa44-b192a4de49f9
date added to LUP
2019-07-08 21:17:06
date last changed
2025-04-04 15:19:23
@inproceedings{aab1d899-c404-40d8-aa44-b192a4de49f9,
  abstract     = {{<p>Tracking of rigid and articulated objects is usually addressed within a particle filter framework or by correspondence based gradient descent methods. We combine both methods, such that (a) the correspondence based estimation gains the advantage of the particle filter and becomes able to follow multiple hypotheses while (b) the particle filter becomes able to propagate the particles in a better manner and thus gets by with a smaller number of particles. Results on noisy synthetic depth data show that the new method is able to track motion correctly where the correspondence based method fails. Further experiments with real-world stereo data underline the advantages of our coupled method.</p>}},
  author       = {{Grest, Daniel and Krüger, Volker}},
  booktitle    = {{Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings}},
  isbn         = {{9783540757023}},
  issn         = {{0302-9743}},
  language     = {{eng}},
  month        = {{12}},
  pages        = {{28--41}},
  publisher    = {{Springer}},
  series       = {{Lecture Notes in Computer Science}},
  title        = {{Gradient-enhanced particle filter for vision-based motion capture}},
  url          = {{http://dx.doi.org/10.1007/978-3-540-75703-0_3}},
  doi          = {{10.1007/978-3-540-75703-0_3}},
  volume       = {{4814}},
  year         = {{2007}},
}