Crane gesture recognition using pseudo 3-D hidden Markov models

Abstract

A recognition technique based on novel pseudo 3D hidden Markov models, which can integrate spatial as well as temporal derived features is presented. The approach allows the recognition of dynamic gestures such as waving hands as well as static gestures such as standing in a special pose. Pseudo 3D hidden Markov models (P3DHMM) are an extension of the pseudo 2D case, which has been successfully used for the classification of images and the recognition of faces. In the P3DHMM case the so-called superstates contain P2DHMM and thus whole image sequences can be generated by these models. Our approach has been evaluated on a crane signal database, which consists of 12 different predefined gestures for maneuvering cranes.