A Challenge-Driven Survey on UAV-Based Target Tracking
Lingyu Jin1,2, Rui Wang1,2, Bo Huang1,2,*
1 College of Optoelectronic Engineering, Chongqing University, Chongqing, China
2 Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China
* Corresponding Author: Bo Huang. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.080050
Received 02 February 2026; Accepted 23 March 2026; Published online 08 April 2026
Abstract
Unmanned Aerial Vehicle (UAV) target tracking is one of the key technologies in aerial intelligent perception systems, playing a vital role in applications such as traffic monitoring, border patrol, disaster response, search and rescue, environmental monitoring, and military reconnaissance. Compared with generic object tracking tasks, UAV platforms exhibit significant differences in imaging perspectives, target scales, motion patterns, and onboard computing capabilities, which pose unique challenges for UAV target tracking, including small targets and drastic scale variations, platform motion and motion blur, complex backgrounds and frequent occlusions, low-light conditions at night, as well as real-time and energy constraints. To address these issues, a large number of UAV-oriented tracking methods have been proposed in recent years, covering traditional correlation filters, deep Siamese networks, and emerging Transformer-based models, achieving continuous performance improvements across multiple UAV benchmark datasets. Despite substantial research efforts, existing survey works primarily focus on generic object tracking or a single technical approach, lacking a systematic summary and comparative analysis from the perspective of UAV application requirements. Unlike previous surveys that mainly classify methods based on model architecture, the innovation of this study lies in establishing a unified UAV target tracking framework centered on five major challenges. First, we analyze typical UAV tracking applications and core challenges. Then, from a challenge-driven perspective, existing methods are categorized and summarized based on small targets and scale variations, rapid motion and motion blur, complex backgrounds and occlusions, low-light night conditions, and lightweight and real-time considerations. Furthermore, we conduct quantitative and qualitative comparisons of representative methods in terms of accuracy, success rate, and computational efficiency on mainstream benchmarks, including UAV123, UAV123@10fps, UAVDT, UAV20L, and DTB70. Finally, we looked ahead to future development directions, such as lightweight deployment, multi-modal fusion, and large model-driven approaches. This work aims to provide a clear technical roadmap and a systematic reference for UAV target tracking research.
Keywords
UAV target tracking; correlation filter (CF); siamese network (SN); transformer; comprehensive survey
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