@InProceedings{BrandãoWainGold:2006:SuHiPa,
author = "Brand{\~a}o, Bruno Cedraz and Wainer, Jacques and Goldenstein,
Siome Klein",
affiliation = "UNICAMP",
title = "Subspace Hierarchical Particle Filter",
booktitle = "Proceedings...",
year = "2006",
editor = "Oliveira Neto, Manuel Menezes de and Carceroni, Rodrigo Lima",
organization = "Brazilian Symposium on Computer Graphics and Image Processing, 19.
(SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "tracking of objects, humans, articulated structures, particle
filtering.",
abstract = "Particle filtering has become a standard tool for non-parametric
estimation in computer vision tracking applications. It is an
instance of stochastic search. Each particle represents a possible
state of the system. Higher concentration of particles at any
given region of the search space implies higher probabilities. One
of its major drawbacks is the exponential growth in the number of
particles for increasing dimensions in the search space. We
present a graph based filtering framework for hierarchical model
tracking that is capable of substantially alleviate this issue.
The method relies on dividing the search space in subspaces that
can be estimated separately. Low correlated subspaces may be
estimated with parallel, or serial, filters and have their
probability distributions combined by a special aggregator filter.
We describe a new algorithm to extract parameter groups, which
define the subspaces, from the system model. We validate our
method with different graph structures withing a simple hand
tracking experiment with both synthetic and real data.",
conference-location = "Manaus, AM, Brazil",
conference-year = "8-11 Oct. 2006",
doi = "10.1109/SIBGRAPI.2006.42",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2006.42",
language = "en",
ibi = "6qtX3pFwXQZG2LgkFdY/LNm7D",
url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/LNm7D",
targetfile = "brandao-SHPF.pdf",
urlaccessdate = "2025, Feb. 10"
}