Tracking human body by using particle filter Gaussian process Markov-switching model

Jing Wang, Hong Man, Yafeng Yin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent variable model and embedded with Markov-switching approach. Training trajectory data is projected from the observation space to the latent space of lower dimensionality in a nonlinear probabilistic manner. In the latent space, particle filter is used to track indeterministic motions of feet. The number of particles are reduced by incorporating learning knowledge as well as temporal information explored by Markovswitching model. The simulation results indicate that the proposed approach is able to effectively track feet with relatively different motion patterns, and even under temporal occlusions.

Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
DOIs
StatePublished - 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

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