  African penguins (Spheniscus demersus) are an endangered species. Little is
known regarding their underwater hunting strategies and associated predation
success rates, yet this is essential for guiding conservation. Modern
bio-logging technology has the potential to provide valuable insights, but
manually analysing large amounts of data from animal-borne video recorders
(AVRs) is time-consuming. In this paper, we publish an animal-borne underwater
video dataset of penguins and introduce a ready-to-deploy deep learning system
capable of robustly detecting penguins<EMAIL_ADDRESS>and also instances of fish
(mAP50@73.3%). We note that the detectors benefit explicitly from air-bubble
learning to improve accuracy. Extending this detector towards a dual-stream
behaviour recognition network, we also provide the first results for
identifying predation behaviour in penguin underwater videos. Whilst results
are promising, further work is required for useful applicability of predation
behaviour detection in field scenarios. In summary, we provide a highly
reliable underwater penguin detector, a fish detector, and a valuable first
attempt towards an automated visual detection of complex behaviours in a marine
predator. We publish the networks, the DivingWithPenguins video dataset,
annotations, splits, and weights for full reproducibility and immediate
usability by practitioners.
