Visual gesture recognition for ground air traffic control using the Radon transform

Abstract

Human gesture recognition is an active topic of vision research which has applications in diverse fields such as collaborative virtual environments and robot teleoperation. We propose a novel method for the recognition of hand gestures, used by air marshals for steering aircraft on the runway, using the Radon transform. Various aspects of the algorithm, including acquisition, segmentation, labeling and recognition using the parametric Radon transform are addressed in this paper. A binary skeleton representation of the human subject is computed. The Radon transform is used to generate maxima corresponding to specific orientations of the skeletal representation. Feature vectors are extracted from the transform space by computing the normalized cumulative projections of the Radon transform on the angle axis. K-means clustering is then applied to recognize static gestures from the extracted features. This technique has the potential to provide information about the exact orientation of gesture segments and can find use in ground control of unmanned air vehicles. Experiments with image data corresponding to the various ground air traffic control gestures used in directing aircrafts, highlight the potential application of this approach.