3D Human Body-Part Tracking and Action Classification Using a Hierarchical Body Model

This paper presents a framework for hierarchical 3D articulated human body-part tracking and action classification. We introduce a Hierarchical Annealing Particle Filter (H-APF) algorithm, which applies nonlinear dimensionality reduction of the high dimensional data space to the low dimensional latent spaces combined with the dynamic motion model and the Hierarchical Human Body Model. The improved annealing approach is used for the propagation between different body models and sequential frames. The tracking algorithm generates trajectories in the latent spaces, which provide low dimensional representations of body poses, observed during the motion. These trajectories are used to classify human motions. The tracking and classification algorithms were checked on HumanEvaI, HumanEvaII, and other datasets, involving more complicated motion types and transitions and proved to be effective and robust. The comparison to other methods and the error calculations are provided.