In this paper, we describe the GPU implementation of a markerless full-body 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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Mussi, L., Ivekovic, S., & Cagnoni, S. (2010). Markerless articulated human body tracking from multi-view video with GPU-PSO. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6274 LNCS, pp. 97–108). https://doi.org/10.1007/978-3-642-15323-5_9
Mendeley helps you to discover research relevant for your work.