SharkTrack: An accurate, generalisable software for streamlining shark and ray underwater video analysis

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. • Introduces SharkTrack, a semi-automatic, human-in-the-loop pipeline for analysing underwater elasmobranch videos • Combines object detection and multi-object tracking to compute species-specific MaxN indices, the standard ecological metric used for underwater elasmobranch video analysis • Achieves 89% ssMaxN accuracy on 207 h of BRUVS footage from unseen locations • Reduces manual video analysis time by approximately 95% compared to traditional methods • Provides an open-source, laptop-ready tool applicable across species and marine ecosystems