Using Gaussian Processes for Human Tracking and Action Classification

Abstract

We present an approach for tracking human body parts and classification of human actions. We introduce Gaussian Processing Annealed Particle Filter Tracker (GPAPF), which is an extension of the annealed particle filter tracker and uses Gaussian Process Dynamical Model (GPDM) in order to reduce the dimensionality of the problem, increase the tracker's stability and learn the motion models. Motion of human body is described by concatenation of low dimensional manifolds which characterize different motion types. The trajectories in the latent space provide low dimensional representations of sequences of body poses performed during motion. Our approach uses these trajectories in order to classify human actions. The approach was checked on HumanEva data set as well as on our own one. The results and the comparison to other methods are presented.