A Novel Taxonomy for Human Activity Recognition Based on a Systematic Analysis of Public UAV Datasets

In recent decades, unmanned aerial vehicles (UAVs) have become widely utilized for many real-world applications, including surveillance, crowd management, and threat detection, providing a new perspective to recognize human behaviors. However, current UAV-based video datasets adopt categorization schemes that rely on broad and inconsistent categories relative to real-world aerial contexts. To address this knowledge gap, this study proposes a novel human activity categorization framework derived from a comprehensive systematic analysis study of ten publicly available UAV-based human action recognition (HAR) datasets, incorporating a variety of environmental situations and human behaviors. By reconciling inconsistent categories and finer activities, this taxonomy serves as a standard framework for UAV-based HAR research. The proposed categorization framework is validated by comparing it with other existing frameworks on the publicly benchmarked Drone-Action dataset, outperforming them by 97% across four metrics. Our contribution aims to develop the foundation for further experimental validation and provide a guide for researchers interested in developing accurate and context-aware surveillance systems.