Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges

TL;DR

This paper reviews vision-based Anti-UAV detection and tracking technologies, analyzes datasets and algorithms, and discusses future research directions to enhance security in complex environments.

Contribution

It provides a comprehensive review of current vision-based Anti-UAV detection and tracking methods, datasets, and outlines future research challenges and opportunities.

Findings

Compilation of publicly available datasets for Anti-UAV research

Analysis of recent vision-based detection and tracking algorithms

Discussion of future research directions in Anti-UAV technology

Abstract

With the rapid advancement of UAV technology and its extensive application in various fields such as military reconnaissance, environmental monitoring, and logistics, achieving efficient and accurate Anti-UAV tracking has become essential. The importance of Anti-UAV tracking is increasingly prominent, especially in scenarios such as public safety, border patrol, search and rescue, and agricultural monitoring, where operations in complex environments can provide enhanced security. Current mainstream Anti-UAV tracking technologies are primarily centered around computer vision techniques, particularly those that integrate multi-sensor data fusion with advanced detection and tracking algorithms. This paper first reviews the characteristics and current challenges of Anti-UAV detection and tracking technologies. Next, it investigates and compiles several publicly available datasets, providing accessible links to support researchers in efficiently addressing related challenges. Furthermore, the paper analyzes the major vision-based and vision-fusion-based Anti-UAV detection and tracking algorithms proposed in recent years. Finally, based on the above research, this paper outlines future research directions, aiming to provide valuable insights for advancing the field.