Feature-based background registration in wide-area motion imagery
Yi Wu, Genshe Chen, Erik Blasch, Bai Li, Haibin Ling · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Image registration in wide area motion imagery (WAMI) is a critical problem that is required for target tracking, image fusion, and situation awareness. The high resolution, extremely low frame rate, and large camera motion in such videos; however, introduces challenging constraints that distinguish the task from traditional image registration from such sesnors as full motion video (FMV). In this study, we propose to use the feature-based approach for the registration of wide area surveillance imagery. Specifically, we extract Speeded Up Robust Feature (SURF) feature points for each frame. After that, a kd-tree algorithm is adopted to match the feature points of each frame to the reference frame. Then, we use the RANdom SAmple Consensus (RANSAC) algorithm to refine the matching results. Finally, the refined matching point pairs are used to estimate the transformation between frames. The experiments are conducted on the Columbus Large Image Format (CLIF) dataset. The experimental results show that the proposed approach is very efficient for the wide area motion imagery registration.