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BK21PLUS Seminar Announcement Feb.16 2017
W-Name Admin (IP: *.152.74.116) W-Date 2017-02-15 15:39 Hit 601

This is to inform of the invited talk held as below. If interested, please come join.

Title:  Switching Gaussian-Wishart Process for Bayesian Inference of Gait

Speaker: Prof. Bong Ki Shin (Pukyong National University)

Date: Feb. 16(Thurs.), 2017

Time: 4:00 PM

Location: Woo Jung Information & Communication bldg., Room 604

Hosted by: Dept. of Brain and Cognitive Engineering, Korea University and Institute for Brain and Cognitive Engineering, Korea University

Contact: 02)3290-5920

Human activity/motion analysis by computers has drawn a lot of attention recently, and one popular tool is Gaussian Process. During the past decade, it has been extended by incorporating latent variables (GPLVM) and dynamic features (GPDM). Another extension involves the integration with a Markov chain to implement nonlinear switching mechanisms across multiple GPs.
This talk compiles a series of studies during the past couple of years over human gait motion analysis. It starts from a factorial HMM  for decoding out gait pose and limb swinging posture dynamics. In the follow-up studies the pose model has been replaced with Gaussian processes modeling the nonlinear pose dynamics. The Bayesian framework is made complete by introducing Wishart processes for the Gaussian observation sequences. The inference is based on the particle filtering algorithm which allows us to approximate the diustribution of pedestrian state. The pedestrian motion is described as a trajectory along Gaussian processes while switching form one to another. The observation vectors are subject to multivariate Gaussians, each of which is given a pair of priors of a Gaussian process for the mean and a Wishart process for the covariance. Experiments have shown an intersting result in decoding human gait.
File 신봉기.pdf(153.7K)

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