Aerial Video Trackers Review.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Robotics

Background:

• Aerial video target tracking faces challenges including image jitter, target blur, high data dimensionality, and scale variation.
• Understanding aerial video target characteristics, background complexity, and tracking diversity is crucial for advancing this technology.

Purpose of the Study:

• To summarize the current research status of aerial video tracking.
• To elaborate on key tracking technologies based on target type, quantity, and scene.
• To classify deep learning-based tracking algorithms by network structure.

Main Methods:

• Systematic review of aerial video tracking research.
• Classification of tracking algorithms based on target type and deep learning network architecture.
• Performance evaluation of common tracking methods on UAV123 and UAV20L datasets.

Main Results:

• Key tracking technologies are detailed for various target types, numbers, and scenes.
• Deep learning tracking algorithms are categorized by network structure.
• Accuracy of common methods is assessed on benchmark aerial datasets.

Conclusions:

• The paper provides a comprehensive overview of aerial video target tracking.
• It identifies current challenges and proposes future research directions.
• Analysis of deep learning methods and dataset evaluations offers insights for algorithm development.