Rethinking Correlation Filter Trackers for Small Unmanned Aircraft Systems

To achieve spatiotemporal continuity or some sparsity for robust tracking, most current discriminative correlation filter (DCF) methods introduce new regularization terms or self-adaption hyperparameters to restrict the trackers. However, regardless of the validity of the pseudo-Gaussian label, previous DCF trackers generally suffer from aberrance, mismatching. In this work, we rethink the DCF tracker from the label matching and propose a label approximation DCF tracker (LACF) focusing on analyzing the commonly used Gaussian pseudo labels in the DCF. Specifically, based on the assumption that the same objects should contain a similar response between two frames, we construct a new pseudo label that combines the original pseudo-Gaussian labels and the previous response map. On the other hand, we introduce a windowing strategy to focus the DCF model on matching crucial labels for the right position. The experimental results demonstrate that LACF significantly achieves competitive performance for real-time CPU small unmanned aircraft tracking.