Aerial video images can be stitched together into a common panoramic image. For that, the global motion between images can be estimated by detecting Harris corner features which are linked to correspondences by a feature tracker. Assuming a planar ground, a homography can be estimated after an appropriate outlier removal. Since Harris features tend to occur clustered at highly structured 3D objects, these features are located in various different planes leading to an inaccurate global motion estimation (gme). Moreover, if only a small number of features is detected or features are located at moving objects, the accuracy of the gme is also negatively affected, leading to severe stitching errors in the panorama. To overcome these issues, we propose: Firstly, the feature correspondences are weighted to approximate a uniform distribution over the image. Secondly, we enforce a fixed number of correspondences of highest possible quality. Thirdly, we propose a temporally variable tracking distance approach to remove outliers located at slowly moving objects. As a result we improve the gme accuracy by 10% for synthetic data and highly reduce the structural dissimilarity (DSSIM) caused by stitching errors from 0.12 to 0.035.
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