Clustering and Tracking-Guided Infrared Spatial–Temporal Small Target Detection

In various civilian and military applications, infrared (IR) small target detection is faced with difficulties because of complex backgrounds, low signal-to-clutter ratio, and clutter interference. Although low-rank and sparse theories based on tensor analysis have been widely employed, three crucial issues persist. The first concerns accurate estimation of target and background, the second involves the development of a more comprehensive target detection model, and the last one is real-time detection performance. This article proposes an IR small target detection method named clustering and tracking-guided infrared small target spatial-temporal prediction completion model (CTSTC). Specifically, a 3-D spatial-temporal tensor is constructed in the high-frequency domain, incorporating target detection priors and subsequent yet-to-be-detected IR frames as foundational data for the prediction completion model. Secondly, we improve K-means clustering algorithm for multiple low-rank background clusters, followed by designing an IKC-derived rank surrogate, resulting in more accurate low-rank background estimation. Furthermore, a Bayesian-inference-derived tracking regularization is incorporated into the completion model to describe the motions and state transitions of targets, thereby improving the robustness of interferences. Meanwhile, an efficient ADMM-based optimization scheme is designed for solving the completion model. Additionally, spatial-temporal evolution-based fusion strategies are proposed, which utilize the past and future spatial-temporal knowledge for the assistance of the detection of current frames and enhance both TD and BS of the prediction completion model. Compared with ten state-of-the-art competitive methods, extensive experiments on five practical datasets have illustrated the superiority of CTSTC in terms of target detectability (TD), background suppressibility (BS), and overall performance.