AI 中文总结
本文介绍CatchMonitor原型系统，利用半监督学习提升物种识别精度，结合简单鲁棒的目标跟踪方法，实现拖网渔船REM视频中鱼类丢弃量的自动量化，并以专家人工量化结果为基准评估性能。
AI 中文摘要
我们报告了CatchMonitor的持续开发进展，该系统现已形成一个原型计算机视觉系统，旨在从拖网渔船上远程电子监控（REM）系统采集的视频素材中自动量化丢弃的鱼类。由于渔船上的真实环境条件，对拖网渔船监控录像的分析是一个具有挑战性的问题。在我们先前工作的基础上，我们通过应用半监督学习提高了物种识别的准确性。我们采用了一种简单且鲁棒的目标跟踪方法，并在此基础上构建了我们的原型丢弃量化系统。最后，我们分析了多位专家人工分析师进行手动丢弃量量化的变异性，并将其作为基准，与我们系统的性能进行比较。
英文摘要
We report on the continued development of CatchMonitor, resulting in a prototype computer vision system designed to automatically quantify discarded fish from video footage collected from Remote Electronic Monitoring (REM) systems on fishing trawlers. The analysis of trawler surveillance footage is a challenging problem due to the real-world conditions on board fishing vessels. Building on our prior work we improve the accuracy of species identification through the application of semi-supervised learning. We utilise a simple and robust object tracking approach, upon which we build our prototype discard quantification system. Finally we analyse the variability of manual discard quantification performed by multiple expert human analysts, using it as a benchmark against which we compare the performance of our system.
Comments30 pages, 5 figures
