Bayesian 3D Human Motion Capture Using Factored Particle Filtering

Abdallah Dib 1 Cédric Rose 1, 2 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We present a markerless human motion capture system that estimates the 3D positions of the body joints over time. The system uses a dynamic bayesian network and a factored particle filtering algorithm. In this paper we evaluate the impact of using different observation functions for the bayesian state estimation: chamfer distance, a pixel intersection and finally a pseudo-observation of the subject direction calculated from the previous output of the system. We also compare two methods for the factored generation of the particles. The first one uses a deterministic interval exploration strategy whereas the second one is based on an adaptive diffusion. The capacity of the system to recover after occlusion by obstacles was tested on simulated movements in a virtual scene.
Type de document :
Communication dans un congrès
22th International Conference on Tools with Artificial Intelligence - ICTAI 2010, Oct 2010, Arras, France. IEEE, 2010
Liste complète des métadonnées

https://hal.inria.fr/inria-00546925
Contributeur : Abdallah Dib <>
Soumis le : mercredi 15 décembre 2010 - 10:34:37
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51

Identifiants

  • HAL Id : inria-00546925, version 1

Collections

Citation

Abdallah Dib, Cédric Rose, François Charpillet. Bayesian 3D Human Motion Capture Using Factored Particle Filtering. 22th International Conference on Tools with Artificial Intelligence - ICTAI 2010, Oct 2010, Arras, France. IEEE, 2010. 〈inria-00546925〉

Partager

Métriques

Consultations de la notice

111