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.
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We want to thank Thor Andreasen for letting us use one of his recording arrays for the experiments, Sara Sofie Thagaard Winther and Odense Zoo for testing our system, Cao Danh Do for manufacturing the camera boxes, Mads Loose Holst and Martin Krusborg Andersen for training the CNN, Iris Adam and Bridget Hallam for reviewing the paper, Coen Elemeans and Michiel Vellema for sharing their biology experience, Centre for BioRobotics for funding the project and our colleagues in SDU-Biorobotics for discussions and support.