A probabilistic graphical model approach for human activity recognition using skeleton data
Amir Hossein Bayat, Mohammad Mahdi Arzani, Mahmood Fathy, Ali Matinnejad, Behrouz Minaei Bidgoli, Rahim Entezari · 2016
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.