%0 Journal Article
%A Cano, Alberto
%A Yeguas-Bolivar, Enrique
%A Munoz-Salinas, Rafael
%A Medina-Carnicer, Rafael
%A Ventura, Sebastian
%T Parallelization strategies for markerless human motion capture
%D 2018
%@ 1861-8200
%U https://hdl.handle.net/10668/28194
%X Markerless motion capture (MMOCAP) is the problem of determining the pose of a person from images captured by one or several cameras simultaneously without using markers on the subject. Evaluation of the solutions is frequently the most time-consuming task, making most of the proposed methods inapplicable in real-time scenarios. This paper presents an efficient approach to parallelize the evaluation of the solutions in CPUs and GPUs. Our proposal is experimentally compared on six sequences of the HumanEva-I dataset using the CMAES algorithm. Multiple algorithm's configurations were tested to analyze the best trade-off with regard to the accuracy and computing time. The proposed methods obtain speedups of 8 in multi-core CPUs, 30 in a single GPU and up to 110 using 4 GPUs.
%K Markerless motion capture (MMOCAP)
%K GPU
%K Tracking
%K 3d human motion
%K Body motion
%K Tracking
%K Stereophotogrammetry
%K Evolution
%~