Full Body Motion Tracking in Monocular Images Using Particle Swarm Optimization

Bogusław Rymut and Tomasz Krzeszowski and Bogdan Kwolek: Full Body Motion Tracking in Monocular Images Using Particle Swarm Optimization; Int. Conf. on Artificial Intelligence and Soft Computing, 2012, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, Vol. 7267, Springer Berlin Heidelberg, 2012, pp. 600-607.

Abstract

The estimation of full body pose in monocular images is a very difficult problem. In 3D-model based motion tracking the challenges arise as at least one-third of degrees of freedom of the human pose that needs to be recovered is nearly unobservable in any given monocular image. In this paper, we deal with high dimensionality of the search space through estimating the pose in a hierarchical manner using Particle Swarm Optimization. Our method fits the projected body parts of an articulated model to detected body parts at color images with support of edge distance transform. The algorithm was evaluated quantitatively through the use of the motion capture data as ground truth.

Citation

@incollection{
    title={Full Body Motion Tracking in Monocular Images Using Particle Swarm Optimization},
    year={2012},
    isbn={978-3-642-29346-7},
    booktitle={Artificial Intelligence and Soft Computing},
    volume={7267},
    series={Lecture Notes in Computer Science},
    editor={Rutkowski, Leszek and Korytkowski, Marcin and Scherer, Rafał and Tadeusiewicz, Ryszard and Zadeh, LotfiA. and Zurada, JacekM.},
    doi={10.1007/978-3-642-29347-4_70},
    url={http://dx.doi.org/10.1007/978-3-642-29347-4_70},
    publisher={Springer Berlin Heidelberg},
    author={Rymut, Bogusław and Krzeszowski, Tomasz and Kwolek, Bogdan},
    pages={600-607}
}

GPU-Supported Object Tracking Using Adaptive Appearance Models and Particle Swarm Optimization

Boguslaw Rymut and Bogdan Kwolek: GPU-Supported Object Tracking Using Adaptive Appearance Models and Particle Swarm Optimization; Int. Conf. on Computer Vision and Graphics, 2010, Computer Vision and Graphics, Lecture Notes in Computer Science, Vol. 6375, Springer Berlin Heidelberg, 2010, pp. 227-234.

Abstract

In this paper we present a particle swarm optimization (PSO) based approach for marker-less full body motion tracking. The objective function is smoothed in an annealing scheme and then quantized. This allows us to extract a pool of candidate best particles. The algorithm selects a global best from such a pool to force the PSO jump out of stagnation. Experiments on 4-camera datasets demonstrate the robustness and accuracy of our method. The tracking is conducted on 2 PC nodes with multi-core CPUs, connected by 1 GigE. This makes our system capable of accurately recovering full body movements with 14 fps.

Citation