Cano, AlbertoYeguas-Bolivar, EnriqueMunoz-Salinas, RafaelMedina-Carnicer, RafaelVentura, Sebastian2025-01-072025-01-072018-02-011861-8200https://hdl.handle.net/10668/28194Markerless 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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Markerless motion capture (MMOCAP)GPUTracking3d human motionBody motionTrackingStereophotogrammetryEvolutionresearch articleopen access10.1007/s11554-014-0467-11861-8219http://helvia.uco.es/xmlui/bitstream/10396/13002/1/cano.pdf427725200013