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.


Published Works

Cano, Alberto and Yeguas-Bolivar, Enrique and Muñoz-Salinas, Rafael and Medina-Carnicer, Rafael and Ventura, Sebastián, «Parallelization strategies for markerless human motion capture»,Journal of Real-Time Image Processing,  pages 1-15, 2014.


Enrique Yeguas-Bolivar, Rafael Muñoz-Salinas, Rafael Medina-Carnicer, Angel Carmona-Poyato, Comparing evolutionary algorithms and particle filters for Markerless Human Motion Capture, Applied Soft Computing, Volume 17, April 2014, Pages 153-166, ISSN 1568-4946,