Bayesian Non-Parametric Inference for Manifold Based MoCap Representation
Fabrizio Natola, Valsamis Ntouskos, Marta Sanzari, Fiora Pirri
Abstract
We propose a novel approach to human action recognition, with motion capture data (MoCap), based on grouping sub-body parts. By representing configurations of actions as manifolds, joint positions are mapped on a subspace via principal geodesic analysis. The reduced space is still highly informative and allows for classification based on a non-parametric Bayesian approach, generating behaviors for each sub-body part. Having partitioned the set of joints, poses relative to a sub-body part are exchangeable, given a specified prior and can elicit, in principle, infinite behaviors. The generation of these behaviors is specified by a Dirichlet process mixture. We show with several experiments that the recognition gives very promising results, outperforming methods requiring temporal alignment.
BibTeX
@inproceedings{iccv2015_bayesiannonparam,
title = {Bayesian Non-Parametric Inference for Manifold Based MoCap Representation},
author = {Fabrizio Natola and Valsamis Ntouskos and Marta Sanzari and Fiora Pirri},
booktitle = {ICCV 2015},
year = {2015}
}