  This paper explores the design and development of a class of robust
diver-following algorithms for autonomous underwater robots. By considering the
operational challenges for underwater visual tracking in diverse real-world
settings, we formulate a set of desired features of a generic diver following
algorithm. We attempt to accommodate these features and maximize general
tracking performance by exploiting the state-of-the-art deep object detection
models. We fine-tune the building blocks of these models with a goal of
balancing the trade-off between robustness and efficiency in an onboard setting
under real-time constraints. Subsequently, we design an architecturally simple
Convolutional Neural Network (CNN)-based diver-detection model that is much
faster than the state-of-the-art deep models yet provides comparable detection
performances. In addition, we validate the performance and effectiveness of the
proposed diver-following modules through a number of field experiments in
closed-water and open-water environments.
