Semi-supervised Visual Tracking of Marine Animals Using Autonomous Underwater Vehicles
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11263_2023_Article_1762.pdf
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5.61 MB
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Author(s)
Cai, Levi
McGuire, Nathan E.
Hanlon, Roger
Mooney, T. A.
Girdhar, Yogesh
Date Issued
March 1, 2023
Publisher
Springer US
Citation
Cai, Levi, McGuire, Nathan E., Hanlon, Roger, Mooney, T. A. and Girdhar, Yogesh. 2023. "Semi-supervised Visual Tracking of Marine Animals Using Autonomous Underwater Vehicles."
Version
Final published version
Abstract
Abstract
In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at
http://warp.whoi.edu/vmat/
, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.
MIT Department
Woods Hole Oceanographic Institution
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11263-023-01762-5
