This paper presents a system which automatically detects moving targets contained in aerial sequences of FLIR images under heavy cluttered conditions. In these situations, the detection of moving targets is generally carried out through the implementation of segmentation and tracking techniques based on the images correlation maintained by the static camera hypothesis. However, detection procedures cannot rely on this correlation when the camera is airborne and, therefore, image stabilization techniques are usually introduced previously to the detection process. Nevertheless, the
use of stabilization algorithms has been often applied to terrestrial sequences and assuming a high computational cost. To overcome these limitations, we propose an innovative and efficient strategy, with a block-based estimation and an affine transformation, operating on a multi-resolution approach for recovering from the ego-motion. Next, once the images have been compensated on the highest resolution image and refined to avoid distortions produced in the sampling process, a dynamic differences-based segmentation followed by a morphological filtering strategy is applied. The novelty of our strategy relies on the relaxation of the pre-assumed hypothesis and, hence, on the enhancement of its
applicability, and also by further reducing its computational cost, thanks to the application of a multi-resolution algorithm. The experiments performed have obtained excellent results and, although the complexity of the system arises, the application of the multi-resolution approach has proved to dramatically reduce the global computational cost.