Understanding the behavior of non-human primates is crucial for improving animal welfare, modeling social behavior, and gaining insights into distinctively human and phylogenetically shared behaviors. However, the lack of datasets on non-human primate behavior hinders in-depth exploration of primate social interactions, posing challenges to research on our closest living relatives. To address these limitations, we present ChimpACT, a comprehensive dataset for quantifying the longitudinal behavior and social relations of chimpanzees within a social group. Spanning from 2015 to 2018, ChimpACT features videos of a group of over 20 chimpanzees residing at the Leipzig Zoo, Germany, with a particular focus on documenting the developmental trajectory of one young male, Azibo. ChimpACT is both comprehensive and challenging, consisting of 163 videos with a cumulative 160,500 frames, each richly annotated with detection, identification, pose estimation, and fine-grained spatiotemporal behavior labels. We benchmark representative methods of three tracks on ChimpACT: (i) tracking and identification, (ii) pose estimation, and (iii) spatiotemporal action detection of the chimpanzees. Our experiments reveal that ChimpACT offers ample opportunities for both devising new methods and adapting existing ones to solve fundamental computer vision tasks applied to chimpanzee groups, such as detection, pose estimation, and behavior analysis, ultimately deepening our comprehension of communication and sociality in non-human primates.

翻译：理解非人灵长类动物的行为对于改善动物福利、建模社会行为以及洞察人类特有和系统发育共享的行为至关重要。然而，非人灵长类动物行为数据集的匮乏阻碍了对灵长类社会互动的深入探索，对我们研究这些最接近的现存亲属构成了挑战。为弥补这些不足，我们提出了ChimpACT，这是一个用于量化社会群体中黑猩猩纵向行为与社会关系的综合性数据集。ChimpACT涵盖2015年至2018年，包含德国莱比锡动物园一个超20只黑猩猩群体的视频，特别聚焦于记录一只年轻雄性黑猩猩Azibo的发育轨迹。该数据集兼具全面性与挑战性，由163个视频组成，累计160,500帧，每帧均标注有检测、识别、姿态估计及细粒度时空行为标签。我们在ChimpACT上对三个任务方向的代表性方法进行了基准测试：（i）跟踪与识别，（ii）姿态估计，以及（iii）黑猩猩的时空动作检测。实验表明，ChimpACT为开发新方法及改进现有方法以解决应用于黑猩猩群体的基础计算机视觉任务（如检测、姿态估计和行为分析）提供了充足机遇，从而深化我们对非人灵长类动物交流与社会性的理解。