A probabilistic graphical model approach for human activity recognition using skeleton data

Abstract

In this paper we introduce a general probabilistic graphical model for human everyday activity recognition. The proposed model is a discriminative graphical model with hidden variables for modeling body pose and sequential order of them. We use a unified framework for prediction task that is faster and more efficient than structured support vector machine and hidden conditional random fields. We have trained and tested the model on RGB-D videos and the result was comparable to the state of the art.