Impact of dynamic model learning on classification of human motion

Vladimir Pavlovic, James M. Rehg

Research output: Contribution to journalConference articlepeer-review

Abstract

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and analysis of figure motion has employed either generic or highly specific hand-tailored dynamic models superficially coupled with hidden Markov models (HMMs) of motion regimes. Recently, an alternative class of learned dynamic models known as switching linear dynamic systems (SLDSs) has been cast in the framework of dynamic Bayesian networks (DBNs) and applied to analysis and tracking of the human figure. In this paper we further study the impact of learned SLDS models on analysis and tracking of human motion and contrast them to the more common HMM models. We develop a novel approximate structured variational inference algorithm for SLDS, a globally convergent DBN inference scheme, and compare it with standard SLDS inference techniques. Experimental results on learning and analysis of figure dynamics from video data indicate the significant potential of the SLDS approach.

Original languageEnglish (US)
Pages (from-to)788-795
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
StatePublished - 2000
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 - Hilton Head Island, SC, USA
Duration: Jun 13 2000Jun 15 2000

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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