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Learning Statistical Models of Human Motion

Bowden, R (2000) Learning Statistical Models of Human Motion In: CVPR 2000 - IEEE Workshop on Human Modeling, Analysis and Synthesis, 2000-07-15 - 2000-07-16, Hilton Head, South Carolina, U.S.A..

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Abstract

Non-linear statistical models of deformation provide methods to learn a priori shape and deformation for an object or class of objects by example. This paper extends these models of deformation to that of motion by augmenting the discrete representation of piecewise nonlinear principle component analysis of shape with a markov chain which represents the temporal dynamics of the model. In this manner, mean trajectories can be learnt and reproduced for either the simulation of movement or for object tracking. This paper demonstrates the use of these techniques in learning human motion from capture data.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Bowden, RUNSPECIFIEDUNSPECIFIED
Date : 15 July 2000
Copyright Disclaimer : © IEEE 2000
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 27 Oct 2016 14:40
Last Modified : 27 Oct 2016 14:40
URI: http://epubs.surrey.ac.uk/id/eprint/812643

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