Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.To this end, we propose a novel framework that combines accurate localization and robust identification of specific keypoints in the image by using a fully convolutional deep architecture.Our algorithm exploits both the field lines and the players’ image locations, assuming their ground plane positions to be given, to achieve accuracy and robustness that is beyond the current state of the art.We will demonstrate its effectiveness on challenging soccer, basketball, and volleyball benchmark datasets.

中文翻译：

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运动场的实时摄像机姿态估计

给定一个图像序列，其中包含运动场和未经校准的相机（例如智能手机之一）拍摄的运动场的一部分，我们的目标是实时自动计算序列中每个图像的焦距和外部相机参数，而无需为此，我们提出了一个新颖的框架，该框架通过使用完全卷积的深层架构将准确的定位和对图像中特定关键点的可靠识别相结合。以及假定球员的地平面位置的图像位置，以实现超出当前技术水平的精度和鲁棒性。我们将展示其在挑战足球，篮球和排球基准数据集方面的有效性。