English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30363
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:


Action spaces for efficient Bayesian tracking of human motion

AuthorsRius, Ignasi; Varona, Javier; Gonzàlez, Jordi; Villanueva, Juan J.
KeywordsPattern recognition::Computer vision
Issue Date2006
PublisherInstitute of Electrical and Electronics Engineers
Citation18th International Conference on Pattern Recognition: pp. 472-475 (2006)
AbstractBayesian tracking implemented as a particle filter is one of the most used techniques for full-body human tracking. However, given the high-dimensionality of the models to be tracked, the number of required particles to properly populate the space of solutions make the problem computationally very expensive. To overcome this, we present an efficient scheme which makes use of an action model that guides the prediction step of the particle filter. In this manner, particles are propagated to locations in the search space with most a posteriori information. Hence, we sample from a smooth motion model only those postures which are feasible given a particular action. We show, that this scheme improves the efficiency and accuracy of the overall tracking approach.
DescriptionInternational Conference on Pattern Recognition (ICPR), 2006, Hong Kong (China)
Appears in Collections:(IRII) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
Action spaces.pdf278,6 kBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.