Elasmobranchs (shark sand rays) represent a critical component of marine ecosystems. Yet, they are experiencing global population declines and effective monitoring of populations is essential to their protection. Underwater stationary videos, such as those from Baited Remote Underwater Video Stations (BRUVS), are critical for understanding elasmobranch spatial ecology and abundance. However, processing these videos requires time-consuming manual analysis that can delay conservation. To address this challenge, we developed SharkTrack, a semi-automatic underwater video analysis software. SharkTrack uses Convolutional Neural Networks (CNN) and Multi-Object Tracking to automatically detect and track elasmobranchs and provides 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 CNN model during training, SharkTrack computed ssMaxN with 89% accuracy over 207 hours of footage. The semi-automatic SharkTrack pipeline required two minutes of manual classification per hour of video, an estimated 95% reduction of manual analysis time compared to traditional methods. Furthermore, we demonstrate SharkTrack accuracy 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, facilitating the analysis of any underwater stationary video. By making video analysis faster and more accessible, SharkTrack enables research and conservation organisations to monitor elasmobranch populations more efficiently, thereby improving conservation efforts. To further support these goals, we provide public access to the SharkTrack software.

翻译：板鳃类（鲨鱼和鳐鱼）是海洋生态系统的关键组成部分。然而，其种群数量正经历全球性下降，对种群进行有效监测对其保护至关重要。水下固定视频（如来自诱饵远程水下视频站（BRUVS）的视频）对于理解板鳃类的空间生态和丰度至关重要。然而，处理这些视频需要耗时的手动分析，可能延误保护行动。为应对这一挑战，我们开发了SharkTrack，一款半自动水下视频分析软件。SharkTrack利用卷积神经网络（CNN）和多目标跟踪技术自动检测和追踪板鳃类动物，并提供标注流程以手动分类板鳃类物种并计算物种特异性最大数量（ssMaxN）——相对丰度的标准度量指标。在CNN模型训练期间未接触过的地点所拍摄的BRUVS影像上进行测试时，SharkTrack在207小时的影像中计算ssMaxN的准确率达到89%。半自动的SharkTrack流程每小时视频仅需两分钟的手动分类时间，相比传统方法估计减少了95%的手动分析时间。此外，我们验证了SharkTrack在不同海洋生态系统和板鳃类物种中的准确性，这相较于先前局限于特定物种或地点的模型是一项进步。SharkTrack的应用不仅限于BRUVS，还可促进任何水下固定视频的分析。通过使视频分析更快、更易获取，SharkTrack使研究和保护组织能够更高效地监测板鳃类种群，从而改进保护工作。为进一步支持这些目标，我们向公众开放了SharkTrack软件。