Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization
This work addresses real-time and size estimation challenges for UAV tracking, which is incremental as it builds on existing filter-based methods with optimizations.
The paper tackled the limitations of UAV visual tracking algorithms in handling size variation and real-time performance by proposing a real-time tracking algorithm with joint scale and aspect ratio optimization, achieving superior results on four UAV benchmarks with computation feasibility on a low-cost CPU.
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.