2014
DOI: 10.1007/s11554-014-0483-1
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Performance evaluation of a 3D multi-view-based particle filter for visual object tracking using GPUs and multicore CPUs

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 … Show more

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Cited by 6 publications
(3 citation statements)
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References 29 publications
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“…Recently, visual tracking has achieved a rapid development on accuracy and robustness rather than speed. There are few work discussing the parallel implementation and optimizations of trackers, especially on heterogeneous platform [21][22][23][24]. Research [23] proposes a high-performance version H-TLD based on OpenMP and CUDA.…”
Section: Parallel Designs and Optimizations Of Visual Trackingmentioning
confidence: 99%
“…Recently, visual tracking has achieved a rapid development on accuracy and robustness rather than speed. There are few work discussing the parallel implementation and optimizations of trackers, especially on heterogeneous platform [21][22][23][24]. Research [23] proposes a high-performance version H-TLD based on OpenMP and CUDA.…”
Section: Parallel Designs and Optimizations Of Visual Trackingmentioning
confidence: 99%
“…The implementation is based on OpenGL and Cg making it difficult to adapt to newer generation devices. GPU implementation of a particle filter-based tracker is described in [3]. It is shown that the GPU version is more effective when the particle count is higher and it can achieve more than 12 times speed-up compared to the multi-core CPU implementation.…”
Section: Related Workmentioning
confidence: 99%
“…The parallel nature of the filter makes it possible to run them on commodity Graphical Processing Units (GPUs) to achieve good performance Choi and Christensen (2013), Concha et al (2014) and Pauwels et al (2013).…”
Section: Introductionmentioning
confidence: 99%