Gesture-based object recognition using histograms of guiding strokes

Abstract

Humans perform iconic gestures to refer to entities through embodying their shapes. For instance, people often gesture the outline of an object (e.g. a circle for a ball) when referring to it during communication. In this paper, we present a gesture-based object recognition algorithm that enables natural human-computer interaction involving iconic gestures. Based on our analysis of multiple gesture performances, we propose a new 3D motion description of iconic gestures, called Histograms of Guiding Strokes (HoGS), which successfully summarizes hand dynamic during gestures. Our gesture-based object recognition algorithm compares favorably to human judgment performance and outperforms most conventional gesture recognition approaches.