3 May 2012 Feature-based background registration in wide-area motion imagery
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Abstract
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.
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Yi Wu, Genshe Chen, Erik Blasch, Li Bai, Haibin Ling, "Feature-based background registration in wide-area motion imagery", Proc. SPIE 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI, 840204 (3 May 2012); doi: 10.1117/12.918804; https://doi.org/10.1117/12.918804
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