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：板鳃类动物（鲨鱼与鳐鱼）是海洋生态系统的关键组成部分。然而，其种群数量正在全球范围内下降，有效的种群监测对其保护至关重要。水下定点视频（如来自诱饵远程水下视频站（BRUVS）的视频）对于理解板鳃类动物的空间生态和丰度至关重要。但处理这些视频需要耗时的人工分析，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）与多目标跟踪技术自动检测并追踪板鳃类动物，同时提供标注流程以人工分类板鳃类物种并计算物种特异性最大数量（ssMaxN）——这是相对丰度的标准度量指标。在CNN训练阶段未接触过的地点采集的BRUVS影像上进行测试时，SharkTrack对207小时影像的ssMaxN计算准确率达到89%。该半自动流程仅需每小时视频投入两分钟人工分类时间，相比传统方法预计可减少95%的人工分析时长。此外，我们验证了SharkTrack在不同海洋生态系统及板鳃类物种间的准确性，这相较于先前局限于特定物种或地点的模型是一大进步。SharkTrack的应用不仅限于BRUVS，还可用于任何水下定点视频的分析。通过使视频分析更快速、更易实施，SharkTrack助力科研与保护机构更高效地监测板鳃类种群，从而提升保护成效。为持续推进这些目标，我们向公众开放SharkTrack软件。