Infrared Search and Track With Unbalanced Optimal Transport Dynamics Regularization

Accurate detection of small and dim targets in infrared imagery is a crucial component in infrared search and track which has broad utility in military and remote sensing applications. Low-rank models have enjoyed state-of-the-art performance in infrared tracking applications, but many approaches underutilize dynamics information which has the potential to improve performance in challenging tracking scenarios. We present two algorithms, robust principal components analysis with patched unbalanced optimal transport (RPCA + PUOT) and robust alignment by sparse and low-rank with patched unbalanced optimal transport (RASL + PUOT), which incorporate optimal transport dynamics regularization and demonstrate improved performance on realistic data.