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.
Dettaglio pubblicazione
2015, 2015 IEEE International Conference on Computer Vision (ICCV), Pages 4606-4614
Bayesian non-parametric inference for manifold based MoCap representation (04b Atto di convegno in volume)
Natola Fabrizio, Ntouskos Valsamis, Sanzari Marta, PIRRI ARDIZZONE Maria Fiora
ISBN: 978-1-4673-8390-5; 978-146738391-2
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