Human activity recognition has been a significant goal of computer vision since its inception and has developed considerably in the last years. Recent approaches to this problem increasingly favour the use of data-driven deep learning methods. To facilitate the comparison of these methods, several datasets pertaining to labelled human activity have been created, having great variation in content and methodology. As the field has developed, the datasets used have undergone considerable evolution as well. In this paper, we attempt to classify and describe a variety of datasets for researchers to choose the most suitable benchmark for their domain. For this, we propose a set of characteristics by which datasets may be compared. We also describe the progress in recent years that sets modern datasets apart from those used in the past.

中文翻译：

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用于人类活动识别的现代数据集的最新发展：深入调查

自计算机视觉诞生以来，人类活动识别一直是其重要目标，并在过去几年取得了长足的发展。最近解决这个问题的方法越来越倾向于使用数据驱动的深度学习方法。为了便于比较这些方法，已经创建了几个与标记的人类活动有关的数据集，在内容和方法上有很大差异。随着该领域的发展，所使用的数据集也经历了相当大的演变。在本文中，我们尝试对各种数据集进行分类和描述，以便研究人员为其领域选择最合适的基准。为此，我们提出了一组可以比较数据集的特征。我们还描述了近年来将现代数据集与过去使用的数据集区分开来的进展。