2D Sensor Based Design of a Dynamic Hand Gesture Interpretation System

Abstract

A complete 2D sensor based system for dynamic gesture interpretation is presented in this paper. A hand model is devised for this purpose, composed of the palm area and the fingertips. Multiple cues are integrated in a feature space. Segmentation is carried out in this space to output the hand model. The robust technique of mean shift mode estimation is used to estimate the parameters of the hand model, making it adaptive and robust. The model is validated in various experiments concerning difficult situations like occlusion, varying illumination, and camouflage. Real time requirements are also met. The gesture interpretation approach refers to dynamic hand gestures. A collection of fingertip locations is collected from the hand model. Tensor voting approach is used to smooth and reconstruct the trajectory. The final output is represented by an encoding sequence of local trajectory directions. These are obtained by mean shift mode detection on the trajectory representation on Radon space. This module was tested and proved highly accurate.