  Autonomous underwater robots working with teams of human divers may need to
distinguish between different divers, e.g. to recognize a lead diver or to
follow a specific team member. This paper describes a technique that enables
autonomous underwater robots to track divers in real time as well as to
reidentify them. The approach is an extension of Simple Online Realtime
Tracking (SORT) with an appearance metric (deep SORT). Initial diver detection
is performed with a custom CNN designed for realtime diver detection, and
appearance features are subsequently extracted for each detected diver. Next,
realtime tracking-by-detection is performed with an extension of the deep SORT
algorithm. We evaluate this technique on a series of videos of divers
performing human-robot collaborative tasks and show that our methods result in
more divers being accurately identified during tracking. We also discuss the
practical considerations of applying multi-person tracking to on-board
autonomous robot operations, and we consider how failure cases can be addressed
during on-board tracking.
