• español
    • English
  • English 
    • español
    • English
  • Login
View Item 
  •   DSpace Home
  • Producción Científica
  • Artículos, capítulos, libros...UCO
  • View Item
  •   DSpace Home
  • Producción Científica
  • Artículos, capítulos, libros...UCO
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Comparing Evolutionary Algorithms and Particle Filters for Markerless Human Motion Capture

Thumbnail
View/Open
Comparing_Evolutionary_Algorithms_and_Particle_Fil.pdf (2.524Mb)
Author
Yeguas-Bolivar, Enrique
Muñoz-Salinas, Rafael
Medina-Carnicer, R.
Carmona Poyato, Ángel
Publisher
Elsevier
Date
2014
Subject
Markerless Motion Capture
Tracking
Particle filters
Evolutionary algorithms
METS:
Mostrar el registro METS
PREMIS:
Mostrar el registro PREMIS
Metadata
Show full item record
Abstract
Markerless Human Motion Capture is the problem of determining the joints’ angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models with a considerable number of degrees of freedom to appropriately adapt to the human anatomy. Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones. Evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization – Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization – with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter. The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested; (ii) they can deal with high-dimensional models thus leading to better accuracy; and (iii) the hierarchical strategy surpasses the holistic one.
URI
http://hdl.handle.net/10396/15688
Fuente
Applied Soft Computing 17, 153-166 (2014)
Versión del Editor
http://dx.doi.org/10.1016/j.asoc.2014.01.007
Collections
  • DIAN-Artículos, capítulos...
  • Artículos, capítulos, libros...UCO

DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
© Biblioteca Universidad de Córdoba
Biblioteca  UCODigital
 

 

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

LoginRegister

Statistics

View Usage Statistics

De Interés

Archivo Delegado/AutoarchivoAyudaPolíticas de Helvia

Compartir


DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
© Biblioteca Universidad de Córdoba
Biblioteca  UCODigital