Self-Supervised Shape Alignment for Sports Field Registration

This paper presents an end-to-end self-supervised learning approach for cross-modality image registration and homography estimation, with a particular emphasis on registering sports field templates onto broadcast videos as a practical application. Rather then using any pairwise labelled data for training, we propose a self-supervised data mining method to train the registration network with a natural image and its edge map. Using an iterative estimation process controlled by a score regression network (SRN) to measure the registration error, the network can learn to estimate any homography transformation regardless of how misaligned the image and the template is. We further show the benefits of using pretrained weights to finetune the network for sports field calibration with few training data. We demonstrate the effectiveness of our proposed method by applying it to real-world sports broadcast videos where we achieve state-of-the-art results and real-time processing.