Spotting dynamic hand gestures in video image sequences using hidden Markov models

Abstract

A new and general stochastic approach to find and identify dynamic gestures in continuous video streams is presented. Hidden Markov models (HMMs) are used to solve this combined problem of temporal segmentation and classification in an integral way. Basically, an improved normalized Viterbi algorithm allows one to continuously observe the output scores of the HMMs at every time step. Characteristic peaks in the output scores of the respective models indicate the presence of gestures. Our experiments in the domain of hand gesture spotting provided excellent recognition results and very low temporal detection delays.