Identity statement area
Reference TypeConference Proceedings
Last Update2006: administrator
Metadata Last Update2020: administrator
Citation KeyBrandãoWainGold:2006:SuHiPa
TitleSubspace Hierarchical Particle Filter
Date8-11 Oct. 2006
Access Date2020, Dec. 04
Number of Files1
Size249 KiB
Context area
Author1 Brandão, Bruno Cedraz
2 Wainer, Jacques
3 Goldenstein, Siome Klein
Affiliation1 UNICAMP
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2008-07-17 14:11:03 :: brunocedraz -> administrator ::
2009-08-13 20:38:08 :: administrator -> banon ::
2010-08-28 20:02:24 :: banon -> administrator ::
2020-02-19 03:17:41 :: administrator -> :: 2006
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Content TypeExternal Contribution
Tertiary TypeFull Paper
Keywordstracking of objects, humans, articulated structures, particle filtering.
AbstractParticle 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.
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Target Filebrandao-SHPF.pdf
User Groupbrunocedraz
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