EAGER - Integrating machine learning on autonomous platforms for target-tracking operations using stereo imagery

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The ocean's midwaters (depths from 200 to 1000 meters where sunlight is dim) are increasingly becoming an area of interest for scientific discovery and study. Efforts to further explore this vast and incredibly important region in the ocean involves the development of small, nimble, autonomous underwater vehicles (AUVs) that can be used for a variety of missions. This proposal will use a large database in video images collected over 25 years to train the vehicle to identify and track targets in real-time using a pair of stereo cameras. This project will involve a Postdoctoral Researcher who will be mentored by collaborators at MBARI and Stanford, who are pioneers in applying machine learning algorithms to underwater imagery. Results of this effort will be disseminated via conferences, publications, and outreach through industry and media partners. Media programs at MBARI and National Geographic Society will produce YouTube videos and social media posts detailing the efforts, the project's personnel, methods, and discoveries.The ocean's midwaters represent the largest ecosystem on earth with unique inhabitants and processes that link the surface waters to the seafloor. Efforts to further explore this vast and incredibly important region in the ocean involves development of AUVs that can be used for a variety of missions (e.g., transecting, tracking, fluid sampling). One of the key vehicle missions for these autonomous vehicles is to track targets in real-time. The tracking missions can be used for science questions as diverse as rates of marine snow sinking and its impact on biogeochemical cycling, the fate of rising methane from the benthos, and direct observations of organismal behavior to address their ecology and biomechanics. In order to conduct these tracking missions, robust algorithms are needed to identify and track targets as they change shape and state in realtime.