LabelBee: a web platform for large-scale semi-automated analysis of honeybee behavior from video

LabelBee: a web platform for large-scale semi-automated analysis of honeybee behavior from video
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LabelBee:用于从视频中大规模半自动分析蜜蜂行为的网络平台

DOI:
10.1145/3359115.3359120
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发表时间:
2019
期刊:
Proceedings of Artificial Intelligence for Data Discovery and Reuse (AIDR’19
影响因子:
--
通讯作者:
Giray, Tugrul
Giray, Tugrul
中科院分区:
--
文献类型:
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作者:
Mégret, Rémi;Rodriguez, Ivan F.;Ford, Isada Claudio;Acuña, Edgar;Agosto-Rivera, Jose L.;Giray, Tugrul

文献摘要

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LabelBee系统是一个Web应用程序,旨在促进视频监控中大量蜜蜂行为数据的收集、注释和分析。它是作为 NSF BIGDATA 项目“蜜蜂自然栖息地行为的大规模多参数分析”的一部分而开发的,我们在该项目中分析蜂群入口的连续视频。由于数据量大且复杂,LabelBee 提供了先进的人工智能和可视化功能,能够构建发现复杂行为模式所需的高质量数据集。它集成了多个级别的信息:原始视频、蜜蜂位置、解码标签、个体轨迹和行为事件(入口/出口、花粉存在、扇动等)。这种集成使得生物学家最终用户能够将手动和自动处理相结合,他们还通过中央服务器共享和更正他们的注释。计算机科学家使用这些注释来创建新的自动模型,并提高自动模块的质量。然后可以将这种半自动化方法构建的数据导出用于分析部分,该部分使用 Jupyter 笔记本在同一服务器上进行,以提取和探索行为模式。
The LabelBee system is a web application designed to facilitate the collection, annotation and analysis of large amounts of honeybee behavior data from video monitoring. It is developed as part of NSF BIGDATA project "Large-scale multi-parameter analysis of honeybee behavior in their natural habitat", where we analyze continuous video of the entrance of bee colonies. Due to the large volume of data and its complexity, LabelBee provides advanced Artificial Intelligence and visualization capabilities to enable the construction of good quality datasets necessary for the discovery of complex behavior patterns. It integrates several levels of information: raw video, honeybee positions, decoded tags, individual trajectories and behavior events (entrance/exit, presence of pollen, fanning, etc.). This integration enables the combination of manual and automatic processing by the biologist end-users, who also share and correct their annotation through a centralized server. These annotations are used by the Computer Scientists to create new automatic models, and improve the quality of the automatic modules. The data constructed by this semi-automatized approach can then be exported for the analytic part, which is taking place on the same server using Jupyter notebooks for the extraction and exploration of behavior patterns.