Correlation Filter

Correlation filters are a powerful class of algorithms used for visual object tracking, aiming to efficiently and accurately estimate an object's trajectory in a video sequence. Current research focuses on improving the discriminative power and efficiency of these filters, often by integrating deep learning techniques such as Siamese networks and employing strategies like channel distillation and contrastive learning to learn more robust and compact feature representations. This work is driven by the need for real-time tracking capabilities, particularly in resource-constrained applications like unmanned aerial vehicle (UAV) tracking, and contributes significantly to advancements in computer vision and related fields.