Abstract
Analyzing animal and human behavior has long been a challenging task in computer vision. Early approaches from the 1970s to the 1990s relied on hand-crafted edge detection, segmentation, and low-level features such as color, shape, and texture to locate objects and infer their identities-an inherently ill-posed problem. Behavior analysis in this era typically proceeded by tracking identified objects over time and modeling their trajectories using sparse feature points, which further limited robustness and generalization. A major shift occurred with the introduction of ImageNet by Deng and Li in 2010, which enabled large-scale visual recognition through deep neural networks and effectively served as a comprehensive visual dictionary. This development allowed object recognition to move beyond complex low-level processing toward learned high-level representations. In this work, we follow this paradigm to build a large-scale Universal Action Space (UAS) using existing labeled human-action datasets. We then use this UAS as the foundation for analyzing and categorizing mammalian and chimpanzee behavior datasets. The source code is released on GitHub at this https URL.
Abstract (translated)
从20世纪70年代到90年代，分析动物和人类的行为一直是计算机视觉中的一个难题。早期的方法依赖于手工设计的边缘检测、分割以及诸如颜色、形状和纹理等低级特征来定位物体并推断其身份——这是一个本质上难以解决的问题。在那个时代的行为分析通常是通过随时间跟踪已识别的对象，并使用稀疏特征点模型它们的轨迹来进行，这进一步限制了系统的鲁棒性和泛化能力。 随着邓和李在2010年引入ImageNet（一个大型图像数据库），情况发生了重大转变。ImageNet使大规模视觉识别成为可能，促进了深度神经网络的应用，并有效地充当了一个全面的视觉词典。这一发展使得物体识别可以超越复杂的低级处理，转向学习高级表示。在这项工作中，我们遵循这一范式，利用现有的标注人类动作数据集构建一个大规模的通用行为空间（UAS）。然后，我们将使用这个UAS作为分析和分类哺乳动物及黑猩猩行为数据集的基础。源代码可在GitHub上发布，网址为[此处提供链接]。
URL
https://arxiv.org/abs/2602.09518
