Hierarchical multi-channel hidden semi Markov graphical models for activity recognition

Recognizing human actions from a stream of unsegmented sensory observations is important for a number of applications such as surveillance and human-computer interaction. A wide range of graphical models have been proposed for these tasks, and are typically extensions of the generative hidden Markov models (HMMs) or their discriminative counterpart, conditional random fields (CRFs). These extensions typically address one of three key limitations in the basic HMM/CRF formalism - unrealistic models for the duration of a sub-event, not encoding interactions among multiple agents directly and not modeling the inherent hierarchical organization of activities. In our work, we present a family of graphical models that generalize such extensions and simultaneously model event duration, multi agent interactions and hierarchical structure. We also present general algorithms for efficient learning and inference in such models based on local variational approximations. We demonstrate the effectiveness of our framework by developing graphical models for applications in automatic sign language (ASL) recognition, and for gesture and action recognition in videos. Our methods show results comparable to state-of-the-art in the datasets we consider, while requiring far fewer training examples compared to low-level feature based methods.