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.

@article{Altuwairqi2025,
  title     = {A Novel Taxonomy for Human Activity Recognition Based on a Systematic Analysis of Public UAV Datasets},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {10},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Sumaya Abdulrahman Altuwairqi and Salma Kammoun Jarraya},
  doi       = {10.14569/IJACSA.2025.0161069},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161069}
}