Coupled hidden Markov models for complex action recognition

We present algorithms for coupling and training hidden Markov models CHMMsl to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However; the Markovian framework makes strong restrictive assumptions about the system generating the signal-that it is a single process having a smalt number of states and an extremely limited stare memory The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.