scholarly journals Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Wang ◽  
Yafeng Yin ◽  
Hong Man
Keyword(s):  
Gaussian Process ◽  
Particle Filter ◽  
Dynamical Model ◽  
Human Tracking ◽  
Gaussian Process Dynamical Model

scholarly journals Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 724 ◽  
Author(s):  
Dmytro Velychko ◽  
Benjamin Knopp ◽  
Dominik Endres
Keyword(s):  
Gaussian Process ◽  
Body Movement ◽  
Human Movement ◽  
Dynamical Model ◽  
Variational Approximation ◽  
Movement Trajectory ◽  
Training Time ◽  
Variational Approximations ◽  
Natural Movement ◽  
Gaussian Process Dynamical Model

We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical Model (CGPDM), which is a latent space coupled dynamical model in discrete time. The purpose of the approximation is threefold: first, to reduce training time of the model; second, to enable modular re-use of learned dynamics; and, third, to store these learned dynamics compactly. Our target applications here are human movement primitive (MP) models, where an MP is a reusable spatiotemporal component, or “module” of a human full-body movement. Besides re-usability of learned MPs, compactness is crucial, to allow for the storage of a large library of movements. We first derive the variational approximation, illustrate it on toy data, test its predictions against a range of other MP models and finally compare movements produced by the model against human perceptual expectations. We show that the variational CGPDM outperforms several other MP models on movement trajectory prediction. Furthermore, human observers find its movements nearly indistinguishable from replays of natural movement recordings for a very compact parameterization of the approximation.

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