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。