SharkTrack: an accurate, generalisable software for streamlining shark and ray underwater video analysis
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Elasmobranchs (sharks and rays) represent a critical component of marine ecosystems. However, they are experiencing global population declines, making effective monitoring essential for their management. Underwater stationary videos, such as those from Baited Remote Underwater Video Stations (BRUVS), are vital for understanding elasmobranch spatial ecology and abundance. However, processing these videos requires time-consuming manual analysis, which can delay conservation efforts. To address this challenge, we developed SharkTrack, a semi-automatic underwater video analysis software. SharkTrack uses Object Detection and Multi-Object Tracking models to automatically detect and track elasmobranchs, providing an annotation pipeline to manually classify elasmobranch species and compute species-specific MaxN (ssMaxN), the standard metric of relative abundance. When tested on BRUVS footage from locations unseen by the model during training, SharkTrack computed ssMaxN with 89% accuracy over 207 h of footage. The semi-automatic SharkTrack pipeline required only two minutes of human labour per hour of video, an estimated 95% reduction in manual analysis time compared to traditional methods. Additionally, we demonstrate the accuracy of SharkTrack across diverse marine ecosystems and elasmobranch species, a significant improvement over previous models, which were limited to specific species or locations. SharkTrack's application extends beyond BRUVS, facilitating the analysis of any underwater stationary video. By making video analysis faster and more accessible, SharkTrack enables research and conservation organizations to monitor elasmobranch populations more efficiently, thereby improving conservation efforts. To further support these goals, we provide public open-access to the SharkTrack software.
Date Issued
2026-05-01
Date Acceptance
2026-03-13
Citation
Ecological Informatics, 2026, 95
ISSN
1574-9541
Publisher
Elsevier
Journal / Book Title
Ecological Informatics
Volume
95
Copyright Statement
© 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.ecoinf.2026.103713
Publication Status
Published
Article Number
103713
Date Publish Online
2026-03-18