3D human tracking with Gaussian process annealed particle filter

We present an approach for tracking human body parts with prelearned motion models in 3D using multiple cameras. We use an annealed particle filter to track the body parts and a Gaussian Process Dynamical Model in order to reduce the dimensionality of the problem, increase the tracker's stability and learn the motion models. We also present an improvement for the weighting function that helps to its use in occluded scenes. We compare our results to the results achieved by a regular annealed particle filter based tracker and show that our algorithm can track well even for low frame rate sequences.