Pose-invariant vehicle identification in aerial electro-optical imagery

In digital airborne electro-optical imagery, the identification of objects, particularly vehicles, has an important role in wide-area search and surveillance applications. We propose an identification and pose estimation approach based on maximising the correlation of features in an image with projections of 3D models. It has been applied to imagery collected in a controlled laboratory environment as well as imagery collected during airborne field trials. The results show good discrimination between different vehicle classes, although performance is degraded by vehicle camouflage and low-resolution imagery. Our approach is scalable, in terms of database size and feature sets, and computationally efficient.