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

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