Recognizing diver hand gestures for human to robot communication underwater

Abstract

The underwater environment provides a range of interesting applications for human-robot teams. A critical issue for such teams is the development of an appropriate communication mechanism between humans and robots operating at depth. Humans operating at depth have developed an applied gesture-based communication language that can be leveraged to enable this communication, but it would be expensive and perhaps impractical to develop a hand-labelled dataset of these gestures to support a machine learning-based approach to the task. To avoid the cost of hand labelling such a large dataset, here we automate the process of collecting a labelled dataset through the use of a simple model trained on a hand-labelled dataset that only identifies salient objects (divers, their heads and hands), and then use a weakly supervised learning process to label a complex set of diver gestures. The result of this process is a system that can recognize a large number of diver hand gestures. Performance of the resulting system is compared against a hand-labelled set of diver gestures.