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Journal of Real-Time Image Processing

, Volume 14, Issue 2, pp 453–467 | Cite as

Parallelization strategies for markerless human motion capture

  • Alberto Cano
  • Enrique Yeguas-Bolivar
  • Rafael Muñoz-SalinasEmail author
  • Rafael Medina-Carnicer
  • Sebastián Ventura
Original Research Paper

Abstract

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\(\times\) in multi-core CPUs, 30\(\times\) in a single GPU and up to 110\(\times\) using 4 GPUs.

Keywords

Markerless motion capture (MMOCAP) GPU Tracking 

Notes

Acknowledgments

This research was supported by the Spanish Ministry of Science and Technology, projects TIN-2011-22408 and TIN-2012-32952, and by FEDER funds. This research was also supported by the Spanish Ministry of Education under FPU grant AP2010-0042.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alberto Cano
    • 1
  • Enrique Yeguas-Bolivar
    • 1
    • 2
  • Rafael Muñoz-Salinas
    • 1
    • 2
    Email author
  • Rafael Medina-Carnicer
    • 1
    • 2
  • Sebastián Ventura
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCórdobaSpain
  2. 2.Maimonides Institute for Biomedical Research (IMIBIC)CórdobaSpain

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