Yin May OO Ankhzaya Jamsrandorj Vanyi Chao 문경률 Kim, Jin wook 2024-01-12T02:44:42Z 2024-01-12T02:44:42Z 2023-11-28 2023-10-17 https://pubs.kist.re.kr/handle/201004/76362 https://ieeexplore.ieee.org/document/10312494 Accurate sports field registration plays a crucial role in computer vision for effective team sports analysis, as it provides the ability to track and recognize the positions, movements, and interactions of players from image space to real-world field space. In this paper, we propose a two-stage deep learning framework for sports field registration. In the first-stage network, we utilize a Residual EfficientNet-Attention U-Net architecture to estimate the initial homography matrix using predefined keypoints to register sports fields. Subsequently, our second-stage network refines the initial homography for improved accuracy. We evaluate our proposed method on the public sports datasets to show its outperform results against the state-of-the-art methods in some evaluation metrics. IEEE Industrial Electronics Society A Residual Attention-based EfficientNet Homography Estimation Model for Sports Field Registration Conference 10.1109/IECON51785.2023.10312494 1 IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society SI Singapore 2023-10-16 Proceedings of IECON 2023