Gesture Recognition for Visually Mediated Interaction using Probabilistic Event Trajectories

Abstract

An approach to gesture recognition is presented in which gestures are modelled probabilistically as sequences of visual events. These events are matched to visual input using probabilistic models estimated from motion feature trajectories. The features used are motion image moments. The method was applied to a set of gestures defined within the context of an application in visually mediated interaction in which they would be used to control an active teleconferencing camera. The approach is computationally efficient, allowing real-time performance to be obtained. 1