BEHAVE - facilitating behaviour coding from videos with AI-detected animals

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2025-03-16 DOI:10.1016/j.ecoinf.2025.103106

Reinoud Elhorst , Martyna Syposz , Katarzyna Wojczulanis-Jakubas

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引用次数: 0

Abstract

Applying video recording to investigate behaviour of wild animals reduces field workload, enhances data accuracy, and minimises disturbance to animals. However, extracting information from collected video data remains a cumbersome and time-consuming task if not, at least partly, automated. Recent advancements in artificial intelligence (AI) offer automatic detection of target animals in video streams, however integrating these detections with software to annotate behaviours is missing. In addition, programs that are able to do these AI detections are often not easy to install or require specialised hardware to run. To address this gap, we introduce BEHAVE, a user-friendly, open-source, free, zero-install tool for coding animal behaviour in video recordings. BEHAVE can use the results of AI detections to skip sections of the video, can extract timestamps from video data, and supports programmable ethograms. The results are saved in a .csv file for further processing. BEHAVE includes a component that allows doing AI detections, on non-specialised hardware, also in a zero-install, user-friendly way. Due to these advantages, the behaviour coding process can be significantly accelerated, resulting in well-organised and readily exportable/importable data.

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.