  With the growth of sensing, control and robotic technologies, autonomous
underwater vehicles (AUVs) have become useful assistants to human divers for
performing various underwater operations. In the current practice, the divers
are required to carry expensive, bulky, and waterproof keyboards or
joystick-based controllers for supervision and control of AUVs. Therefore,
diver action-based supervision is becoming increasingly popular because it is
convenient, easier to use, faster, and cost effective. However, the various
environmental, diver and sensing uncertainties present underwater makes it
challenging to train a robust and reliable diver action recognition system. In
this regard, this paper presents DARE, a diver action recognition system, that
is trained based on Cognitive Autonomous Driving Buddy (CADDY) dataset, which
is a rich set of data containing images of different diver gestures and poses
in several different and realistic underwater environments. DARE is based on
fusion of stereo-pairs of camera images using a multi-channel convolutional
neural network supported with a systematically trained tree-topological deep
neural network classifier to enhance the classification performance. DARE is
fast and requires only a few milliseconds to classify one stereo-pair, thus
making it suitable for real-time underwater implementation. DARE is
comparatively evaluated against several existing classifier architectures and
the results show that DARE supersedes the performance of all classifiers for
diver action recognition in terms of overall as well as individual class
accuracies and F1-scores.
