Quantification of behaviors in macaques provides crucial support for various scientific disciplines, including pharmacology, neuroscience, and ethology. Despite recent advancements in the analysis of macaque behavior, research on multi-label behavior detection in socially housed macaques, including consideration of interactions among them, remains scarce. Given the lack of relevant approaches and datasets, we developed the Behavior-Aware Relation Network (BARN) for multi-label behavior detection of socially housed macaques. Our approach models the relationship of behavioral similarity between macaques, guided by a behavior-aware module and novel behavior classifier, which is suitable for multi-label classification. We also constructed a behavior dataset of rhesus macaques using ordinary RGB cameras mounted outside their cages. The dataset included 65 913 labels for 19 behaviors and 60 367 proposals, including identities and locations of the macaques. Experimental results showed that BARN significantly improved the baseline SlowFast network and outperformed existing relation networks. In conclusion, we successfully achieved multi-label behavior detection of socially housed macaques with both economic efficiency and high accuracy.
猕猴行为的量化在许多领域的研究中提供了关键的实验支持，如药物安全评估、神经科学和动物行为学。近些年来，许多研究人员致力于猕猴的行为分析。然而，很少有工作研究群居猕猴的多标签行为检测。该任务考虑了猕猴之间的交互，更符合群居猕猴的特征。鉴于缺乏和该任务相关的方法和数据集，在该文中我们提出了一个用于群居猕猴多标签行为检测的行为感知关系网络（BARN）。该网络在一个行为感知模块的指导下建模了猕猴之间的行为相似性这一关系，还包含一个新颖的更适用于多标签分类的行为分类器。我们还利用安装在笼子外的普通RGB相机构建了一个恒河猴的行为数据集。数据注释包含65 913个对应于19种行为的标签和60 367个包括猕猴的身份和位置的提议（proposals）。实验结果表明BARN对基线网络SlowFast取得了显著改进并且由于现有的关系网络。总的来说，我们以低成本和高精度成功地完成了群居猕猴的多标签行为检测。.
Keywords: Behavioral similarity; Drug safety assessment; Macaque behavior; Multi-label behavior detection; Relation network.