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Performance evaluation of a 3D multi-view-based particle filter for visual object tracking using GPUs and multicore CPUs

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Abstract

This paper presents a deep and extensive performance analysis of the particle filter (PF) algorithm for a very compute intensive 3D multi-view visual tracking problem. We compare different implementations and parameter settings of the PF algorithm in a CPU platform taking advantage of the multithreading capabilities of the modern processors and a graphics processing unit (GPU) platform using NVIDIA CUDA computing environment as developing framework. We extend our experimental study to each individual stage of the PF algorithm, and evaluate the quality versus performance trade-off among different ways to design these stages. We have observed that the GPU platform performs better than the multithreaded CPU platform when handling a large number of particles, but we also demonstrate that hybrid CPU/GPU implementations can run almost as fast as only GPU solutions.

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Acknowledgments

This research has been partially supported by the Spanish Government Projects Refs. TIN2011-28151 and TIN2014-57633 and NVIDIA Professor Partnership Program.

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Authors and Affiliations

  1. Department of Computer Science, Universidad Rey Juan Carlos, C/Tulipán, s/n, 28933, Móstoles, Madrid, Spain

    David Concha, Raúl Cabido, Juan José Pantrigo & Antonio S. Montemayor

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  1. David Concha
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  2. Raúl Cabido
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  3. Juan José Pantrigo
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  4. Antonio S. Montemayor
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Correspondence to Antonio S. Montemayor.

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Concha, D., Cabido, R., Pantrigo, J.J. et al. Performance evaluation of a 3D multi-view-based particle filter for visual object tracking using GPUs and multicore CPUs. J Real-Time Image Proc 15, 309–327 (2018). https://doi.org/10.1007/s11554-014-0483-1

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  • DOI: https://doi.org/10.1007/s11554-014-0483-1

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