Acquiring high resolution quantitative behavioural data underwater often involves installation of costly infrastructure, or capture and manipulation of animals. Aquatic movement ecology can therefore be limited in taxonomic range and ecological coverage. Here we present a novel deep-learning based, multi-individual tracking approach, which incorporates Structure-from-Motion in order to determine the 3D location, body position and the visual environment of every recorded individual. The application is based on low-cost cameras and does not require the animals to be confined, manipulated, or handled in any way. Using this approach, single individuals, small heterospecific groups and schools of fish were tracked in freshwater and marine environments of varying complexity. Positional tracking errors as low as 1.09 ± 0.47 cm (RSME) in underwater areas up to 500 m2 were recorded. This cost-effective and open-source framework allows the analysis of animal behaviour in aquatic systems at an unprecedented resolution. Implementing this versatile approach, quantitative behavioural analysis can be employed in a wide range of natural contexts, vastly expanding our potential for examining non-model systems and species.

中文翻译：

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高分辨率、非侵入性动物追踪和水生生态系统局部环境重建。

在水下获取高分辨率定量行为数据通常涉及安装昂贵的基础设施，或捕获和操纵动物。因此，水生运动生态学可以在分类范围和生态覆盖范围内受到限制。在这里，我们提出了一种新颖的基于深度学习的多个体跟踪方法，它结合了运动结构，以确定每个记录个体的 3D 位置、身体位置和视觉环境。该应用程序基于低成本相机，不需要以任何方式限制、操纵或处理动物。使用这种方法，在不同复杂性的淡水和海洋环境中追踪单个个体、小型异种群体和鱼群。位置跟踪误差低至 1.09 ± 0。在最大 500 m2 的水下区域中记录了 47 cm (RSME)。这种具有成本效益的开源框架允许以前所未有的分辨率分析水生系统中的动物行为。实施这种通用方法，定量行为分析可以在广泛的自然环境中使用，极大地扩展了我们检查非模型系统和物种的潜力。