Bayesian Non-Parametric Inference for Manifold Based MoCap Representation

Fabrizio Natola, Valsamis Ntouskos, Marta Sanzari, Fiora Pirri; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 4606-4614

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.

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[bibtex]
@InProceedings{Natola_2015_ICCV,
author = {Natola, Fabrizio and Ntouskos, Valsamis and Sanzari, Marta and Pirri, Fiora},
title = {Bayesian Non-Parametric Inference for Manifold Based MoCap Representation},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}