Progressive Representation Learning for Real-Time UAV Tracking

TL;DR

This paper introduces PRL-Track, a novel progressive learning framework for UAV tracking that improves robustness in complex environments by combining coarse and fine representation learning, achieving high accuracy and real-time performance.

Contribution

The work proposes a new hierarchical representation learning framework with appearance and semantic regulators for improved UAV tracking in dynamic environments.

Findings

Achieves superior tracking accuracy on three UAV benchmarks.

Operates at 42.6 frames per second on a typical UAV platform.

Demonstrates robustness against aspect ratio changes and occlusion.

Abstract

Visual object tracking has significantly promoted autonomous applications for unmanned aerial vehicles (UAVs). However, learning robust object representations for UAV tracking is especially challenging in complex dynamic environments, when confronted with aspect ratio change and occlusion. These challenges severely alter the original information of the object. To handle the above issues, this work proposes a novel progressive representation learning framework for UAV tracking, i.e., PRL-Track. Specifically, PRL-Track is divided into coarse representation learning and fine representation learning. For coarse representation learning, two innovative regulators, which rely on appearance and semantic information, are designed to mitigate appearance interference and capture semantic information. Furthermore, for fine representation learning, a new hierarchical modeling generator is developed to intertwine coarse object representations. Exhaustive experiments demonstrate that the proposed PRL-Track delivers exceptional performance on three authoritative UAV tracking benchmarks. Real-world tests indicate that the proposed PRL-Track realizes superior tracking performance with 42.6 frames per second on the typical UAV platform equipped with an edge smart camera. The code, model, and demo videos are available at \url{https://github.com/vision4robotics/PRL-Track}.