PoHMM-based human action recognition

Abstract

In this paper we approach the human action recognition task using the Product of Hidden Markov Models (PoHMM). This approach allow us to get large state-space models from the normalized product of several simple HMMs. We compare this mixed graphical model with other directed multi-chain models like Coupled Hidden Markov Model (CHMM) or Factorial Hidden Markov Model (FHMM), so as with Conditional Random Field (CRF), a particular case of undirected graphical models. Our results show that PoHMM outperforms the classification score of these other space-state models on the KTH database using optical flow features.