Discriminative correlation tracking based on spatial attention mechanism for low-resolution imaging systems

Abstract

Low-resolution images are characterized by blurring, less texture information, and lack of detail. Visual object tracking for low-resolution imaging systems remains a challenging task. In this paper, we propose a discriminative correlation tracking algorithm based on a spatial attention mechanism for low-resolution imaging systems (LSDCT) to address these challenges. The key innovations of our proposed algorithm include adjustable windows and a spatial attention mechanism. We design a generic adjustable window to mitigate boundary effects and employ the spatial attention mechanism to highlight the target in low-resolution images. We conduct qualitative and quantitative evaluations on three well-known benchmark datasets: OTB100, TC128, and UAV123. Extensive experimental results indicate that the proposed approach is superior to state-of-the-art trackers.