Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function. Automatic recognition of animals and their behaviors is critical for capitalizing on the large unlabeled datasets generated by modern video devices and for accelerating monitoring efforts at scale. However, the development of automated recognition systems is currently hindered by a lack of appropriately labeled datasets. Existing video datasets 1) do not classify animals according to established biological taxonomies; 2) are too small to facilitate large-scale behavioral studies and are often limited to a single species; and 3) do not feature temporally localized annotations and therefore do not facilitate localization of targeted behaviors within longer video sequences. Thus, we propose MammalNet, a new large-scale animal behavior dataset with taxonomy-guided annotations of mammals and their common behaviors. MammalNet contains over 18K videos totaling 539 hours, which is ~10 times larger than the largest existing animal behavior dataset. It covers 17 orders, 69 families, and 173 mammal categories for animal categorization and captures 12 high-level animal behaviors that received focus in previous animal behavior studies. We establish three benchmarks on MammalNet: standard animal and behavior recognition, compositional low-shot animal and behavior recognition, and behavior detection. Our dataset and code have been made available at: https://mammal-net.github.io.

翻译：监测动物行为可通过提供关于野生动物健康、种群状况及生态系统功能的关键信息，为保护工作提供支持。动物及其行为的自动识别对于利用现代视频设备生成的大量未标记数据集、加速规模化监测具有关键意义。然而，当前自动化识别系统的发展因缺乏适当标注的数据集而受阻。现有视频数据集存在以下问题：1）未依据既有的生物分类学对动物进行分类；2）规模过小，难以支撑大规模行为研究，且通常局限于单一物种；3）未包含时间定位标注，因此无法在较长视频序列中定位目标行为。为此，我们提出MammalNet——一个基于分类学指导的哺乳动物及其常见行为标注的大规模动物行为数据集。该数据集包含超过1.8万段视频，总时长539小时，约为现有最大动物行为数据集的10倍。在动物分类方面，它涵盖17个目、69个科及173个哺乳动物类别；在行为标注方面，收录了此前动物行为研究中聚焦的12类高级动物行为。我们基于MammalNet建立了三项基准任务：标准动物与行为识别、组合式小样本动物与行为识别、行为检测。数据集与代码已发布于：https://mammal-net.github.io。