Model-Based 3D Human Motion Capture Using Global-Local Particle Swarm Optimizations
We present an approach for tracking the articulated motion of humans using image sequences obtained from multiple calibrated cameras. A 3D human body model composed of eleven segments, which allows both rotation at joints and translation, is utilized to estimate the pose. We assume that the initial pose estimate is available. A modified swarm intelligence based searching scheme is utilized to perform motion tracking. At the beginning of each optimization cycle, we estimate the pose of the whole body and then we refine locally the limb poses using smaller number of particles. The results that were achieved in our experiments are compared with those produced by other state-of-the-art methods, with analyses carried out both through qualitative visual evaluations as well as quantitatively by the use of the motion capture data as ground truth. They indicate that our method outperforms the algorithm based on the ordinary particle swarm optimization.
KeywordsParticle Swarm Optimization Motion Capture Motion Tracking Motion Capture Data Particle Swarm Optimiza
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