Tracking and Classifying of Human Motions with Gaussian Process Annealed Particle Filter

Abstract

This paper presents a framework for 3D articulated human body tracking and action classification. The method is based on nonlinear dimensionality reduction of high dimensional data space to low dimensional latent space. Motion of human body is described by concatenation of low dimensional manifolds which characterize different motion types. We introduce a body pose tracker, which uses the learned mapping function from low dimensional latent space to high dimensional body pose space. The trajectories in the latent space provide low dimensional representations of body poses performed during motion. They are used to classify human actions. The approach was checked on HumanEva dataset as well as on our own one. The results and the comparison to other methods are presented.