论文标题
自动故障恢复和重新定位，用于通过联合规模和宽高比优化的在线无人机跟踪
Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization
论文作者
论文摘要
目前的无人机（UAV）视觉跟踪算法主要受到以下方面的限制：（i）它们可以处理的大小变化的种类，（ii）实现速度几乎不符合实时需求。在这项工作中，提出了具有强大尺寸估计能力的实时无人机跟踪算法。具体而言，总体跟踪任务分配给两个2D滤波器：（i）用于空间域中位置预测的翻译过滤器，（ii）大小域中的规模和纵横比优化的大小过滤器。此外，引入了有效的两阶段重新检测策略，用于长期无人机跟踪任务。在四个无人机基准上进行的大规模实验证明了所提出的方法的优越性，该方法在低成本CPU上具有可行性。
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.
