Infrared small moving target detection algorithm based on joint spatio-temporal sparse recovery

Abstract

A dim small moving target detection algorithm based on joint spatio-temporal sparse recovery is proposed in this paper. A spatio-temporal over-complete dictionary is firstly trained from infrared image sequence, and it can characterize not only motion information but also morphological feature. In the spatio-temporal over-complete dictionary, the spatio-temporal atom is then classified as target spatio-temporal atom building target spatio-temporal over-complete dictionary, which describes moving target, and background spatio-temporal atom constructing background spatio-temporal over-complete dictionary, which embeds background clutter. Infrared image sequence is decomposed on the union of target spatio-temporal over-complete dictionary and background spatio-temporal over-complete dictionary. The residual reconstructed by its homologous spatio-temporal over-complete dictionary is very little, yet the residual recovered by its heterogonous spatio-temporal over-complete dictionary is quite large. Therefore, their residuals after decomposing and reconstruction by the joint spatio-temporal sparse recovery would differ so distinctly that it is adopted to decide the signal is from target or background. Some experiments are induced and the experimental results show this proposed approach could not only improve the sparsity more efficiently, but also enhance the target detection performance more effectively.