Person Recognition in Aerial Surveillance: A Decade Survey

2025-11-21

AI Summary

This survey systematically reviews person detection, identification, and re-identification research using aerial platforms—particularly UAVs—from 2014 to 2024. It addresses inherent challenges in high-altitude surveillance, including severe viewpoint skew, small object scales, heavy occlusion, and large pose variations. Methodologically, it comparatively analyzes modeling disparities between aerial and ground-based scenarios from computer vision and machine learning perspectives, synthesizing over 150 publications to trace methodological evolution. The work consolidates 12 publicly available aerial person datasets, exposing critical gaps among data curation, algorithm design, and real-world deployment. As a key contribution, it introduces the first unified taxonomy covering all three tasks—detection, identification, and re-identification—and identifies three pivotal future directions: multi-source feature alignment, lightweight cross-view modeling, and robustness enhancement for realistic operational environments.

Technology Category

• Computer Vision: Image and Video Retrieval
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Surveying person recognition challenges in aerial surveillance over 10 years
• Analyzing computer vision methods for human detection and identification from drones
• Addressing unique aerial perspective difficulties compared to ground-based surveillance

Innovation

• Surveying computer vision for aerial person recognition
• Analyzing drone-based human detection and identification
• Reviewing machine learning approaches for aerial surveillance