Extended Infrared Target Filtering via Random Finite Set and Low-Rank Matrix Decomposition

Target detection in infrared remote sensing images has important practical applications. Among the current high-performance methods, the deep learning-based methods require training samples, and their generalization ability is also limited by the training set. The separation of low-rank and sparse matrix requires joint processing of multiple images with high computational complexity. The track-before-detect algorithms based on particle filtering also have high computational complexity. In this paper, the low-rank and sparse matrix of a single image are proposed for target detection, and a differentiable objective function is used in the separation. At the same time, an extended multitarget tracking algorithm based on random sets is used for target filtering between frames, and the design of the filters adopts the conjugate distribution under the Bayesian framework. Finally, the practical infrared sequence images containing multiple targets and complex backgrounds were employed to verify the performance of the proposed algorithms by comparing them with state-of-the-art algorithms.