PoHMM-based human action recognition

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.