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2008
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Tracking human body by using particle filter Gaussian process Markov-switching model

10 years 1 months ago
Tracking human body by using particle filter Gaussian process Markov-switching model
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.
Jing Wang, Hong Man, Yafeng Yin
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Jing Wang, Hong Man, Yafeng Yin
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