Markerless articulated human body tracking from multi-view video with GPU-PSO

Authors:
Luca Mussi;Spela Ivekovic;Stefano Cagnoni
Affiliations:
Dept. of Information Engineering, University of Parma, Italy;Dept. of Information Engineering, University of Parma, Italy and Lessells Scholar, Royal Society of Edinburgh, Scotland;Dept. of Information Engineering, University of Parma, Italy
Venue:
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Year:
2010

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Abstract

In this paper, we describe the GPU implementation of a markerless fullbody articulated human motion tracking system from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation problem solved using particle swarm optimisation (PSO). We model the human body pose with a skeleton-driven subdivision-surface human body model. The optimisation 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. In formulating the solution, we exploit the inherent parallel nature of PSO to formulate a GPU-PSO, implemented within the nVIDIA™ CUDA™ architecture. Results demonstrate that the GPU-PSO implementation recovers the articulated body pose from 10-viewpoint video sequences with significant computational savings when compared to the sequential implementation, thereby increasing the practical potential of our markerless pose estimation approach.