Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization

TL;DR

This paper presents a real-time UAV tracking algorithm that effectively handles size variations and re-initializes after failures, using joint scale and aspect ratio optimization with a two-filter approach.

Contribution

The work introduces a novel real-time UAV tracking method with joint size and aspect ratio optimization and a two-stage re-detection strategy for long-term tracking.

Findings

Outperforms existing methods on four UAV benchmarks.

Operates efficiently on low-cost CPUs.

Demonstrates robust size variation handling.

Abstract

Current unmanned aerial vehicle (UAV) visual tracking algorithms are primarily limited with respect to: (i) the kind of size variation they can deal with, (ii) the implementation speed which hardly meets the real-time requirement. In this work, a real-time UAV tracking algorithm with powerful size estimation ability is proposed. Specifically, the overall tracking task is allocated to two 2D filters: (i) translation filter for location prediction in the space domain, (ii) size filter for scale and aspect ratio optimization in the size domain. Besides, an efficient two-stage re-detection strategy is introduced for long-term UAV tracking tasks. Large-scale experiments on four UAV benchmarks demonstrate the superiority of the presented method which has computation feasibility on a low-cost CPU.