This paper addresses the significant challenge of recognizing behaviors in
non-human primates, specifically focusing on chimpanzees. Automated behavior
recognition is crucial for both conservation efforts and the advancement of
behavioral research. However, it is significantly hindered by the
labor-intensive process of manual video annotation. Despite the availability of
large-scale animal behavior datasets, the effective application of machine
learning models across varied environmental settings poses a critical
challenge, primarily due to the variability in data collection contexts and the
specificity of annotations.
In this paper, we introduce ChimpBehave, a novel dataset featuring over 2
hours of video (approximately 193,000 video frames) of zoo-housed chimpanzees,
meticulously annotated with bounding boxes and behavior labels for action
recognition. ChimpBehave uniquely aligns its behavior classes with existing
datasets, allowing for the study of domain adaptation and cross-dataset
generalization methods between different visual settings. Furthermore, we
benchmark our dataset using a state-of-the-art CNN-based action recognition
model, providing the first baseline results for both within and cross-dataset
settings. The dataset, models, and code can be accessed at:
this https URL

本论文介绍了 ChimpBehave，一个包含超过 2 小时视频（约 193,000 帧）的新型数据集，用于动作识别，通过与现有数据集对齐，可以研究领域自适应和跨数据集泛化方法，并使用最先进的基于 CNN 的动作识别模型提供了首个基准结果。

从森林到动物园：利用 ChimpBehave 进行大型猿类行为识别

From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave

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.

理解非人类灵长类动物的行为对于改善动物福利，建模社会行为以及获取独特的人类和谱系共享行为的洞见至关重要。然而，非人类灵长类动物行为数据集的缺乏阻碍了对灵长类动物社会互动的深入探索，给研究我们最亲近的近亲带来了挑战。为了解决这些限制，我们提出了 ChimpACT，这是一个全面的数据集，用于量化社会群体中黑猩猩的纵向行为和社会关系。ChimpACT 涵盖了 2015 年到 2018 年，包括德国莱比锡动物园的 20 多只黑猩猩群体的视频，特别关注一个年轻雄性 Azibo 的发展轨迹。ChimpACT 非常全面且具有挑战性，包括 163 个视频，累积包含 16 万 5000 帧，每帧都进行了检测、识别、姿势估计和细粒度的时空行为标注。我们在 ChimpACT 上对三个任务进行了代表性方法的评估：(i) 跟踪和识别，(ii) 姿势估计，以及 (iii) 黑猩猩的时空动作检测。我们的实验表明，ChimpACT 为设计新方法和调整现有方法解决应用于黑猩猩群体的基本计算机视觉任务（如检测、姿势估计和行为分析）提供了充足的机会，从而最终加深我们对非人类灵长类动物的沟通和社会性的理解。