ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors
Xiaoxuan Ma Stephan P. Kaufhold Jiajun Su Wentao Zhu Jack Terwilliger Andres Meza Yixin Zhu Federico Rossano Yizhou Wang
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.