2D Hand Tracking Based on Flocking with Obstacle Avoidance

Abstract

Hand gesture-based interaction provides a natural and powerful means for human-computer interaction. It is also a good interface for human-robot interaction. However, most of the existing proposals are likely to fail when they meet some skin-coloured objects, especially the face region. In this paper, we present a novel hand tracking method which can track the features of the hand based on the obstacle avoidance flocking behaviour model to overcome skin-coloured distractions. It allows features to be split into two groups under severe distractions and merge later. The experiment results show that our method can track the hand in a cluttered background or when passing the face, while the Flocking of Features (FoF) and the Mean Shift Embedded Particle Filter (MSEPF) methods may fail. These results suggest that our method has better performance in comparison with the previous methods. It may therefore be helpful to promote the use of the hand gesture-based human-robot interaction method.