Pseudo 3-D HMMs for image sequence recognition

In this paper, a novel approach to image sequence recognition is presented. We refer to this approach as pseudo 3-D Hidden Markov modeling, a technique which can integrate spatial as well as temporal derived features in an elegant and efficient way. This allows the recognition of dynamic gestures such as waving hands as well as more static gestures such as standing in a special pose. Pseudo 3-D Hidden Markov Models (P3DHMMs) are a natural extension of the pseudo 2-D case, which has been successfully used for the classification of images. In the P3DHMM case the so-called superstates contain P2DHMMs and thus whole image sequences can be generated by these models. The feasibility of our approach is demonstrated in this paper by a number of experiments on a crane signal database, which consists of 12 different predefined gestures for maneuvering cranes. To our knowledge, this is the first publication which reports about the usage of pseudo 3-D Hidden Markov Models.