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.

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