The latest advances in artificial intelligence technology have opened doors to the video analysis of complex behaviours. In light of this, ethologists are actively exploring the potential of these innovations to streamline the time-intensive behavioural analysis process using video data. Several tools have been developed for this purpose in primatology in the past decade. Nonetheless, each tool grapples with technical constraints. To address these limitations, we have established a comprehensive protocol designed to harness the capabilities of a cutting-edge artificial intelligence-assisted software, LabGym. The primary objective of this study was to evaluate the suitability of LabGym for the analysis of primate behaviour, focusing on Japanese macaques as our model subjects. First, we developed a model that accurately detects Japanese macaques, allowing us to analyse their actions using LabGym. Our behavioural analysis model succeeded in recognising stone-handling-like behaviours on video. However, the absence of quantitative data within the specified time frame limits the ability of our study to draw definitive conclusions regarding the quality of the behavioural analysis. Nevertheless, to the best of our knowledge, this study represents the first instance of applying the LabGym tool specifically for the analysis of primate behaviours, with our model focusing on the automated recognition and categorisation of specific behaviours in Japanese macaques. It lays the groundwork for future research in this promising field to complexify our model using the latest version of LabGym and associated tools, such as multi-class detection and interactive behaviour analysis.

摘要

人工智能技术的最新进展为复杂行为的视频分析打开了大门。有鉴于此，动物行为学家正在积极探索这些创新的潜力，以简化使用视频数据的耗时的行为分析过程。在过去的十年里，灵长类动物学领域已经为此目的开发了几种工具。尽管如此，每个工具都面临着技术限制。为了解决这些限制，我们建立了一个全面的协议，旨在利用尖端人工智能辅助软件 LabGym 的功能。本研究的主要目的是评估 LabGym 分析灵长类动物行为的适用性，重点关注日本猕猴作为我们的模型对象。首先，我们开发了一个能够准确检测日本猕猴的模型，使我们能够使用 LabGym 分析它们的行为。我们的行为分析模型成功地识别了视频中类似处理石头的行为。然而，在指定时间范围内缺乏定量数据限制了我们的研究得出有关行为分析质量的明确结论的能力。尽管如此，据我们所知，这项研究代表了第一个专门应用 LabGym 工具来分析灵长类动物行为的实例，我们的模型专注于日本猕猴特定行为的自动识别和分类。它为这一前景广阔的领域的未来研究奠定了基础，即使用最新版本的 LabGym 和相关工具（例如多类检测和交互行为分析）来复杂化我们的模型。