Feature Block-Aware Correlation Filters for Real-Time UAV Tracking

Recently, by virtue of the high computational efficiency and accuracy, discriminative correlation filter (DCF)-based tracking methods have gained attraction in the field of unmanned aerial vehicle (UAV). However, conventional DCF-based methods merely rely on cyclic shift to produce training samples. As a result, the filter trained by these samples owns limited discriminative ability, ineffectively addressing various challenges in the tracking stage. Here, to promote the filter's discriminative ability, we develop a feature block-aware correlation filter (CF) method. Specifically, the extracted feature is divided into two blocks, i.e., target and background feature blocks. These blocks only contain target and background features, respectively, by using different mask matrixes. Then, two regularization terms are proposed to combine both feature blocks into the DCF framework. In addition, we employ effective channel reliability weights to generate target response for precise positioning. Furthermore, substantial experiments have been accomplished on multiple public UAV benchmarks, proving that our tracker possesses superior tracking capabilities and operates at similar to 40 frames per second (FPS) on the CPU platform.