Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning Abstract journal article ['941c8243-8acf-4a9e-af07-ded5acdc35f6'] Polar Data Catalogue https://www.polardata.ca/pdcinput/public/keywordlibrary 1.0 Drone Systems and Applications 2022 10.1139/juvs-2021-0024 Harasyn, Madison Personal Madison.harasyn@umanitoba.ca Centre for Earth Observation Science - University of Manitoba https://orcid.org/0000-0002-5741-6766 ORCID http://orcid.org/ Chan, Wayne Personal wayne.chan@umanitoba.ca Centre for Earth Observation Science - University of Manitoba Ausen, Emma Personal emma.ausen@umanitoba.ca Centre for Earth Observation Science - University of Manitoba Barber, David Personal david.barber@umanitoba.ca Centre for Earth Observation Science - University of Manitoba 0000-0001-9466-3291 ORCID http://orcid.org/ Creative Commons Attribution 4.0 International Open CC-BY-4.0 SPDX https://spdx.org/licenses English Freshwater Beluga Unmanned Aerial Vehicle computer vision deep learning object detection object tracking Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning Churchill Beluga Boat Drone Imagery related journal article published in Drone Systems and Applications. DOI: https://doi.org/10.1139/juvs-2021-0024 PDF application/pdf 4104522.0 One Beluga, Two Beluga, Three Beluga, Four: How to Count Belugas When You Run Out of Fingers and Toes Researchers at CEOS are often asked to write a field story about their work, to make their research more accessible. We decided to do something a little different for our work on applying machine learning to detecting and tracking beluga whales: we are presenting it as a comic-book style video!