Fast Simplex-HMM for One-Shot Learning Activity Recognition

Abstract

The work presented in this paper deals with the challenging task of learning an activity class representation using a single sequence for training. Recently, Simplex-HMM framework has been shown to be an efficient representation for activity classes, however, it presents high computational costs making it impractical in several situations. A dimensionality reduction of the features spaces based on a Maximum at Posteriori adaptation combined with a fast estimation of the optimal parameters in the Expectation Maximization algorithm are presented in this paper. As confirmed by the experimental results, these two modifications not only reduce the computational cost but also maintain the performance or even improve it. The process suitability is experimentally confirmed using the human activity datasets Weizmann, KTH and IXMAS and the gesture dataset ChaLearn.