Abstract
Infrared (IR) small target detection against complex backgrounds is one of the most important tasks in infrared search and tracking systems. Achieving a high detection rate and a low false alarm rate against complex backgrounds remains challenging in practical applications. In this letter, we propose a novel small target detection method based on a local hypergraph dissimilarity measure (LHDM). As an alternative to the unstable dissimilarity in conventional simple graphs, a novel probabilistic hypergraph dissimilarity is presented to capture high-order affinity relationships among neighbors. Then, the corresponding LHDM in a local nested window is constructed based on the hypergraph model to enhance small targets and suppress complex backgrounds. The final saliency map is calculated via max-pooling of the LHDM at multiple scales. Finally, we utilize an adaptive threshold for target segmentation. The results of a series of experiments and evaluations performed on six real IR sequences demonstrate that the LHDM performs favorably compared to several baseline methods.
Published Version
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