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Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis
Sensors ( IF 3.4 ) Pub Date : 2021-05-07 , DOI: 10.3390/s21093237
Rodrigo Cupertino Bernardes 1 , Maria Augusta Pereira Lima 2 , Raul Narciso Carvalho Guedes 1 , Clíssia Barboza da Silva 3 , Gustavo Ferreira Martins 4
Affiliation  

Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses’ possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields.

中文翻译:


基于计算机视觉和人工智能的自动行为分析软件



手动监测动物行为既耗时又容易产生偏差。解决此类限制的另一种方法是在行为评估中使用计算资源(例如跟踪系统)来促进准确和长期的评估。人们需要强大的软件来解决异构环境中的分析(例如在现场条件下)并评估群体中的多个个体,同时保持他们的身份。 Ethoflow 软件是使用计算机视觉和人工智能 (AI) 工具开发的,可以自动监控各种行为参数。实现了基于实例分割的对象检测算法,允许异构环境下的现场行为监控。此外,还实现了卷积神经网络来评估复杂行为,从而扩展了行为分析的可能性。描述了用于自动生成人工智能模型训练数据的启发式方法,并且使用这些数据集训练的模型在检测异构环境中的个体和评估复杂行为方面表现出很高的准确性。 Ethoflow 用于运动学评估和检测群居蜜蜂的交足轴。该软件是在桌面应用程序中开发的,并具有图形用户界面。在Ethoflow算法中,人工智能处理与其他模块分开,便于在普通计算机上进行测量以及在具有图形处理单元的机器上进行复杂行为评估。 Ethoflow 是生物学及相关领域应用的有用支持工具。
更新日期:2021-05-07
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