Real-time pose tracking based on a 3D skeletal model using multiple cameras

In this work a robust algorithm for tracking an object and its pose is developed when its 3D skeletal model is known. Proposed system works in real-time using particle filters on low dimensional manifolds captured by Gaussian process latent variable model (GPLVM). In this experimental work we rendered our ground truth silhouettes for generating the perfecly segmented data. A synthetically generated walking sequence from recorded motion capture data is tracked in real-time using a standard personal computer.