Markerless Articulated Human Body Tracking from Multi-view Video with GPU-PSO

Mussi, Luca; Iveković, Špela; Cagnoni, Stefano

doi:10.1007/978-3-642-15323-5_9

Cited by 19 publications

(34 citation statements)

References 23 publications

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“…The method is directly based on the sequential approach presented in [22]. PSO is the most popular algorithm in parallel implementations because of its inherent parallel nature [32,38,50]. Nonetheless, some works like [43] apply other easily parallelizable metaheuristics (e.g.…”

Section: Parallelization Approachesmentioning

confidence: 99%

Parallelization strategies for markerless human motion capture

Cano

Yeguas-Bolivar

Muñoz-Salinas

et al. 2014

J Real-Time Image Proc

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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 in 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.

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Section: Parallelization Approachesmentioning

confidence: 99%

Parallelization strategies for markerless human motion capture

Cano

Yeguas-Bolivar

Muñoz-Salinas

et al. 2014

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…The paper of (Mussi et al, 2010) presents a GPU-based implementation of a markerless fullbody articulated human motion tracking system. The body reconstruction is based on image sequences from multiple cameras.…”

Section: Human Pose Reconstructionmentioning

confidence: 99%

Cognitive Robotics in Industrial Environments

Puls¹,

Graf²,

Wörn³

2012

Human Machine Interaction - Getting Closer

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“…However, the technical research capable of recognizing the common body action and meeting the needed speed of real time interaction on the hardware cannot meet the requirements [9], [17][18]. Along the appearance of depth camera , the work is simplified greatly [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…The error function has multiple local limit values. During the error minimizing process, the intelligent optimization algorithm, such as particle swarm [9], hierarchy particle swarm [10], hierarchical evolutionary [2] and the like are used for solving global optimum. And the algorithm has poor real-time.…”

Section: Introductionmentioning

confidence: 99%