In the study of animal behaviour, annotation and analysis is largely done manually either directly in the field or from recordings. An emerging field, computational ethology, is challenging this approach by using machine learning to automate the process. However, the use of such methods in general is complicated by a lack of modularity, leading to high cost and long development times. At the same time, the benefits of implementing a fully automated pipeline are often minuscule. We propose online analysis as a way to gain more from automating the process, such as making it easier to ensure that equipment is properly configured and calibrated, enabling the recording equipment to follow the animals, and even enabling closed-loop experiments. In this work, we discuss the requirements and challenges for such a system and propose an implementation based on modern IT infrastructure. Finally, we demonstrate the system in case studies of bats and mongoose. As more and more methods and algorithms are developed we expect online systems to enable new experimental setups to study behaviour, leading to new insights in the field.

在动物行为研究中，注释和分析很大程度上是直接在野外或从记录中手动完成的。通过使用机器学习来自动化过程，新兴的计算伦理学领域正在挑战这种方法。但是，由于缺乏模块性，因此通常使用这种方法变得复杂，导致高成本和长开发时间。同时，实施全自动管道的好处通常是微不足道的。我们建议进行在线分析，以从过程自动化中获得更多收益，例如使其更容易确保正确配置和校准设备，使记录设备能够跟随动物，甚至进行闭环实验。在这项工作中，我们讨论了这种系统的要求和挑战，并提出了基于现代IT基础架构的实施方案。最后，我们在蝙蝠和猫鼬的案例研究中演示了该系统。随着越来越多的方法和算法的发展，我们期望在线系统能够启用新的实验设置来研究行为，从而带来该领域的新见解。