  Elasmobranchs (sharks and rays) can be important components of marine
ecosystems but are experiencing global population declines. Effective
monitoring of these populations is essential to their protection. Baited Remote
Underwater Video Stations (BRUVS) have been a key tool for monitoring, but
require time-consuming manual analysis. To address these challenges, we
developed SharkTrack, an AI-enhanced BRUVS analysis software. SharkTrack uses
Convolutional Neural Networks and Multi-Object Tracking to detect and track
elasmobranchs and provides an annotation pipeline to manually classify
elasmobranch species and compute MaxN, the standard metric of relative
abundance. We tested SharkTrack on BRUVS footage from locations unseen by the
model during training. SharkTrack computed MaxN with 89% accuracy over 207
hours of footage. The semi-automatic SharkTrack pipeline required two minutes
of manual classification per hour of video, a 97% reduction of manual BRUVS
analysis time compared to traditional methods, estimated conservatively at one
hour per hour of video. Furthermore, we demonstrate SharkTrack application
across diverse marine ecosystems and elasmobranch species, an advancement
compared to previous models, which were limited to specific species or
locations. SharkTrack applications extend beyond BRUVS analysis, facilitating
rapid annotation of unlabeled videos, aiding the development of further models
to classify elasmobranch species. We provide public access to the software and
an unprecedentedly diverse dataset, facilitating future research in an
important area of marine conservation.
