Person Recognition in Aerial Surveillance: A Decade Survey

TL;DR

This survey reviews over a decade of research on aerial human recognition, analyzing datasets, challenges, and methods used in drone-based surveillance to inform future advancements.

Contribution

It provides a comprehensive systematic review of aerial human recognition tasks, datasets, challenges, and approaches, highlighting gaps and future research directions.

Findings

Identification of unique aerial surveillance challenges

Compilation of publicly available aerial datasets

Analysis of current methods and their limitations

Abstract

The rapid emergence of airborne platforms and imaging sensors is enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment, and covert observation capabilities. This paper provides a comprehensive overview of 150+ papers over the last 10 years of human-centric aerial surveillance tasks from a computer vision and machine learning perspective. It aims to provide readers with an in-depth systematic review and technical analysis of the current state of aerial surveillance tasks using drones, UAVs, and other airborne platforms. The object of interest is humans, where human subjects are to be detected, identified, and re-identified. More specifically, for each of these tasks, we first identify unique challenges in performing these tasks in an aerial setting compared to the popular ground-based setting and subsequently compile and analyze aerial datasets publicly available for each task. Most importantly, we delve deep into the approaches in the aerial surveillance literature with a focus on investigating how they presently address aerial challenges and techniques for improvement. We conclude the paper by discussing the gaps and open research questions to inform future research avenues.