College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, China.
MAIS, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Zool Res. 2023 Nov 18;44(6):1026-1038. doi: 10.24272/j.issn.2095-8137.2022.485.
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.
对猕猴行为进行量化为药理学、神经科学和行为学等多个科学领域提供了重要支持。尽管最近在分析猕猴行为方面取得了进展，但对社交圈养猕猴的多标签行为检测的研究，包括对它们之间相互作用的考虑，仍然很少。鉴于缺乏相关的方法和数据集，我们开发了用于社交圈养猕猴的多标签行为检测的行为感知关系网络 (BARN)。我们的方法通过行为感知模块和新颖的行为分类器来模拟猕猴之间行为相似性的关系，这适合于多标签分类。我们还使用安装在笼子外的普通 RGB 摄像机构建了猕猴行为数据集。该数据集包含 65913 个标签，用于 19 种行为和 60367 个建议，包括猕猴的身份和位置。实验结果表明，BARN 显著提高了基线 SlowFast 网络的性能，并优于现有的关系网络。总之，我们成功地实现了经济高效且具有高精度的社交圈养猕猴的多标签行为检测。
