Capturing Global and Local Dynamics for Human Action Recognition

Human action analysis has achieved great success especially with the recent development of advanced sensors and algorithms that can effectively track the body joints. Temporal motion of body joints carries crucial information about human actions. However, current dynamic models typically assume stationary local transition and therefore are limited to local dynamics. In contrast, we propose a novel human action recognition algorithm that is able to capture both global and local dynamics of joint trajectories by combining a Gaussian-Binary restricted Boltzmann machine (GB-RBM) with a hidden Markov model (HMM). We present a method to use RBM as a generative model for multi-class classification. Experimental results on benchmark datasets demonstrate the capability of the proposed method in exploiting the dynamic information at different levels.