Homography estimation is often an indispensable step in computer vision tasks that require multi-frame time-domain information. However, when we estimate the traditional homography matrix, the rotational and translational terms are often difficult to balance. In this paper, based on the 4-point homography parameter matrix, we reproduce the Synthetic COCO dataset (S-COCO) and the Photometrically Distorted Synthetic COCO dataset (PDS-COCO). Then, we use the Darknet in YOLOv3 as the backbone to design a deep network for 4-point homography estimation. Experiments show that compared with existing main one-stop methods, our proposed deep learning network achieves the best performance on the S-COCO dataset and excellent performance on the PDS-COCO dataset.
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