Gaussian ringlet intensity distribution (GRID) features for rotation-invariant object detection in wide area motion imagery

Most detection algorithms are established by using well defined features. Since wide area imagery is low resolution and has features that are not well defined, a local intensity distribution based methodology seems a likely candidate. We propose a new methodology, Gaussian Ringlet Intensity Distribution (GRID), which is a derivative of the ring-partitioned histograms for local intensity distribution based object tracking in low-resolution environments, which deals with the issue of rotation invariance. We observed that the proposed algorithm produces the highest accuracy among other state of the art methodologies and provides robust features for rotationally invariant detection and tracking in wide area motion imagery.