Biologically inspired multilevel approach for multiple moving targets detection from airborne forward-looking infrared sequences.

In this paper, a biologically inspired multilevel approach for simultaneously detecting multiple independently moving targets from airborne forward-looking infrared (FLIR) sequences is proposed. Due to the moving platform, low contrast infrared images, and nonrepeatability of the target signature, moving targets detection from FLIR sequences is still an open problem. Avoiding six parameter affine or eight parameter planar projective transformation matrix estimation of two adjacent frames, which are utilized by existing moving targets detection approaches to cope with the moving infrared camera and have become the bottleneck for the further elevation of the moving targets detection performance, the proposed moving targets detection approach comprises three sequential modules: motion perception for efficiently extracting motion cues, attended motion views extraction for coarsely localizing moving targets, and appearance perception in the local attended motion views for accurately detecting moving targets. Experimental results demonstrate that the proposed approach is efficient and outperforms the compared state-of-the-art approaches.