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Related Experiment Videos

Architectural Evolution of UAV Tracking Under Efficiency Constraints.

Yuxuan Huang¹, Dongyu Lu², Xinyi Bo²

¹School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This summary is machine-generated.

This review analyzes Unmanned Aerial Vehicle (UAV) tracking architectures, focusing on balancing performance and computational limits. It highlights Mamba/SSM models and discusses trade-offs for efficient aerial surveillance and perception.

Keywords:

Mamba Transformer UAV tracking efficiency onboard deployment review state-space model

Related Experiment Videos

Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Unmanned Aerial Vehicle (UAV) tracking is crucial for applications like aerial surveillance and autonomous perception.
Progress in UAV tracking is hindered by the trade-off between tracking robustness and onboard computational limitations.
Existing surveys often overlook the architectural evolution under efficiency constraints.

Purpose of the Study:

To examine UAV tracking from the perspective of architectural evolution under efficiency constraints.
To incorporate and analyze Mamba- and State Space Model (SSM)-based trackers.
To provide a cross-paradigm analysis of tracking methods including Correlation Filter (CF), Convolutional Neural Network (CNN), and Transformer models.

Main Methods:

Discussing UAV tracking as a deployment-constrained problem.
Analyzing various tracking paradigms (CF, Siamese/CNN, Transformer, Mamba/SSM) from a cross-paradigm viewpoint.
Examining how different architectures balance representation, interaction, temporal modeling, and deployment cost.

Main Results:

Heterogeneous evaluation settings necessitate careful interpretation of benchmark results.
Recent architectures, including Mamba/SSM, are evaluated for their efficiency and performance trade-offs.
Architectural paradigms demonstrate varying capabilities in addressing UAV tracking constraints.

Conclusions:

Summarizing key architecture-level trade-offs in UAV tracking.
Identifying open challenges in sequence modeling, efficiency evaluation, hardware-aware design, and multimodal tracking.
Highlighting the need for efficient and robust UAV tracking solutions.