Recording animal behaviour is an important step in evaluating the well-being of animals and further understanding the natural world. Current methods for documenting animal behaviour within a zoo setting, such as scan sampling, require excessive human effort, are unfit for around-the-clock monitoring, and may produce human-biased results. Several animal datasets already exist that focus predominantly on wildlife interactions, with some extending to action or behaviour recognition. However, there is limited data in a zoo setting or data focusing on the group behaviours of social animals. We introduce a large meerkat (Suricata Suricatta) behaviour recognition video dataset with diverse annotated behaviours, including group social interactions, tracking of individuals within the camera view, skewed class distribution, and varying illumination conditions. This dataset includes videos from two positions within the meerkat enclosure at the Wellington Zoo (Wellington, New Zealand), with 848,400 annotated frames across 20 videos and 15 unannotated videos.

翻译：记录动物行为是评估动物福祉及进一步理解自然世界的重要环节。当前在动物园环境中记录动物行为的方法（如扫描取样法）需要过多人力投入，不适用于全天候监测，且可能产生人类偏见的结果。现有多个动物数据集主要聚焦于野生动物交互行为，部分已扩展至动作或行为识别领域。然而，针对动物园场景或群居动物群体行为的数据仍十分有限。我们提出一个大型猫鼬（Suricata Suricatta）行为识别视频数据集，包含多样化的标注行为类型，涵盖群体社交互动、个体在镜头视野内的追踪、偏斜的类别分布及多变的光照条件。该数据集包含惠灵顿动物园（新西兰惠灵顿）猫鼬展区内两个视角的视频，共计20段视频中的848,400个标注帧以及15段未标注视频。