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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Krzeszowski, T., Kwolek, B., Rymut, B., Wojciechowski, K., Josinski, H. (2012). Real-Time Tracking of Full-Body Motion Using Parallel Particle Swarm Optimization with a Pool of Best Particles. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_12
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DOI: https://doi.org/10.1007/978-3-642-29353-5_12
Publisher Name: Springer, Berlin, Heidelberg
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