  In this paper, we present an approach to reconstruct 3-D human motion from
multi-cameras and track human skeleton using the reconstructed human 3-D point
(voxel) cloud. We use an improved and more robust algorithm, probabilistic
shape from silhouette to reconstruct human voxel. In addition, the annealed
particle filter is applied for tracking, where the measurement is computed
using the reprojection of reconstructed voxel. We use two different ways to
accelerate the approach. For the CPU only acceleration, we leverage Intel TBB
to speed up the hot spot of the computational overhead and reached an
accelerating ratio of 3.5 on a 4-core CPU. Moreover, we implement an
intensively paralleled version via GPU acceleration without TBB. Taking account
all data transfer and computing time, the GPU version is about 400 times faster
than the original CPU implementation, leading the approach to run at a
real-time speed.
