Graphical modeling of conditional random fields for human motion recognition

Modeling and understanding human motions are challenging in computer vision areas because the similar motions often occur at various time moments. The long-term dependences in observation data should be modeled to improve motion recognition performance. The conditional random field (CRF) is a powerful mechanism for large-span data modeling. In this paper, we present a new graphical model approach to effectively and efficiently implement CRF. Specifically, we integrate the dependent variables of a graph into a clique and build the junction tree for complex CRF structure with cycles. Using this approach, a tree inference algorithm is developed for finding the joint probability of all variables in the clique tree. In the implementation, we specify the continuous-valued hidden Markov model (HMM) parameters as the feature functions and evaluate the proposed junction tree CRF (JT-CRF) by using CMU Graphics Lab Motion Capture Database. The experimental results show that JT-CRF achieves the highest classification accuracies compared to the HMM, the maximum entropy Markov model and the linear-chain CRF.