In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.

翻译：海洋生物的原位视觉观测对于理解其行为及其与周围生态系统的关系至关重要。传统上，这些观测通过潜水员、标签以及遥控或人工驾驶水下载具完成。然而近年来，配备摄像机和具有GPU能力的嵌入式计算机的自主水下航行器正被开发用于多种应用场景，尤其可在人工操作或标签使用较为困难的领域补充现有数据采集机制。现有方法多采用全监督跟踪技术，但许多水下物种的标注数据严重匮乏。半监督跟踪器因其所需数据量少于全监督方法，可能提供替代性解决方案。然而，由于目前缺乏真实的水下跟踪数据集，半监督跟踪算法在海洋领域的性能尚不明确。为更好评估其性能与实用性，本文提出：(1) 一个专用于海洋动物的新型数据集（访问地址：http://warp.whoi.edu/vmat/）；(2) 针对水下动物跟踪场景，对当前先进半监督算法进行评估；(3) 通过在半监督算法中搭载自主水下航行器对野生海洋动物进行跟踪的实际演示，评估其真实环境下的性能表现。