十年来空中人体识别技术进展全景图，助你快速掌握核心挑战与方向
Person Recognition in Aerial Surveillance: A Decade Survey
- 系统梳理150+篇论文，聚焦无人机等空中平台的人体检测与识别
- 对比空地场景差异，分析10年来的公开数据集与主流算法方案
- 揭示当前技术瓶颈，为未来研究指明关键突破口
空中平台和成像传感器的快速发展，使大规模、高机动性、隐蔽性强的空中监控成为可能。本文从计算机视觉与机器学习视角，对过去十年中以人类为中心的空中监控任务进行了全面综述，涵盖150余篇相关论文。研究对象为人，目标是实现空中环境中人体的检测、识别与再识别。针对每一项任务，首先分析其在空中场景相较于地面场景的独特挑战，并整理和评估现有公开数据集。重点深入探讨文献中的方法，分析其如何应对空中特有难题及改进路径。最后，总结当前研究空白与开放问题，为未来研究提供方向指引。
原文摘要 · Abstract (English)
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.
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