Impact of dynamics on subspace embedding and tracking of sequences

Kooksang Moon, Vladimir Pavlović

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

In this paper we study the role of dynamics in dimensionality reduction problems applied to sequences. We propose a new family of marginal auto-regressive (MAR) models that describe the space of all stable auto-regressive sequences, regardless of their specific dynamics. We apply the MAR class of models as sequence priors in probabilistic sequence subspace embedding problems. In particular, we consider a Gaussian process latent variable approach to dimensionality reduction and show that the use of MAR priors may lead to better estimates of sequence subspaces than the ones obtained by traditional non-sequential priors. We then propose a learning method for estimating nonlinear dynamic system (NDS) models that utilizes the new MAR priors. The utility of the proposed methods is demonstrated on several synthetic datasets as well as on the task of tracking 3D articulated figures in monocular image sequences.

Original languageEnglish (US)
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages198-205
Number of pages8
DOIs
StatePublished - 2006
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
ISSN (Print)1063-6919

Other

Other2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Country/TerritoryUnited States
CityNew York, NY
Period6/17/066/22/06

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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