A Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes

Abstract

Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). Qualitative and quantitative experimental analysis in various scenes demonstrates the superior detection performance and efficiency of the proposed algorithm for detecting infrared small moving targets in complex scenes.