Recent evolution of modern datasets for human activity recognition: a deep survey

Abstract

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. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.