Multi-View Human Body Pose Estimation with CUDA-PSO

Multi-View Human Body Pose Estimation with CUDA-PSO

Luca Mussi (Henesis s.r.l., Parma, Italy), Spela Ivekovic (Department of Mechanical & Aerospace Engineering, University of Strathclyde, Glasgow, UK), Youssef S.G. Nashed (Department of Information Engineering, University of Parma, Parma, Italy) and Stefano Cagnoni (Department of Information Engineering, University of Parma, Parma, Italy)
DOI: 10.4018/jaras.2012100104


The authors formulate the body pose estimation as a multi-dimensional nonlinear optimization problem, suitable to be approximately solved by a meta-heuristic, specifically, the particle swarm optimization (PSO). Starting from multi-view video sequences acquired in a studio environment, a full skeletal configuration of the human body is retrieved. They use a generic subdivision-surface body model in 3-D to generate solutions for the optimization problem. PSO then looks for the best match between the silhouettes generated by the projection of the model in a candidate pose and the silhouettes extracted from the original video sequence. The optimization method, in this case PSO, is run in parallel on the Graphics Processing Unit (GPU) and is implemented in Cuda-C™ on the nVidia CUDA™ architecture. The authors compare the results obtained by different configurations of the camera setup, fitness function, and PSO neighborhood topologies.
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In this section, we review recent work relevant to our approach. We begin with the related research in articulated human body pose estimation and then review the basics of PSO and research in the area of PSO parallelization.

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