Institute for Geoinformatics and Institute for Computer Science, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany.
Department of Computer Science, University of Sheffield, Western Bank, Sheffield S102TN, UK.
Sci Adv. 2023 Apr 21;9(16):eadg2094. doi: 10.1126/sciadv.adg2094.
Quantifying the behavior of small animals traversing long distances in complex environments is one of the most difficult tracking scenarios for computer vision. Tiny and low-contrast foreground objects have to be localized in cluttered and dynamic scenes as well as trajectories compensated for camera motion and drift in multiple lengthy recordings. We introduce CATER, a novel methodology combining an unsupervised probabilistic detection mechanism with a globally optimized environment reconstruction pipeline enabling precision behavioral quantification in natural environments. Implemented as an easy to use and highly parallelized tool, we show its application to recover fine-scale motion trajectories, registered to a high-resolution image mosaic reconstruction, of naturally foraging desert ants from unconstrained field recordings. By bridging the gap between laboratory and field experiments, we gain previously unknown insights into ant navigation with respect to motivational states, previous experience, and current environments and provide an appearance-agnostic method applicable to study the behavior of a wide range of terrestrial species under realistic conditions.
量化在复杂环境中长途穿越的小动物的行为是计算机视觉中最具挑战性的跟踪场景之一。在杂乱和动态的场景中，必须定位微小的和低对比度的前景对象，并且还需要补偿多个长时间记录中相机运动和漂移的轨迹。我们引入了 CATER，这是一种将无监督概率检测机制与全局优化环境重建管道相结合的新方法，能够在自然环境中实现精确的行为量化。我们将其实现为一个易于使用且高度并行化的工具，并展示了它在从非约束性野外记录中恢复自然觅食沙漠蚂蚁的精细运动轨迹方面的应用，这些轨迹已注册到高分辨率图像镶嵌重建中。通过弥合实验室和野外实验之间的差距，我们获得了关于蚂蚁在动机状态、先前经验和当前环境下导航的以前未知的见解，并提供了一种与现实条件下广泛的陆地物种的行为研究相关的、与外观无关的方法。
