论文标题
实时摄像头姿势估计运动场
Real-Time Camera Pose Estimation for Sports Fields
论文作者
论文摘要
给定一个图像序列，其中包含一部分运动场，该运动场是由移动和未校准的摄像机（例如智能手机之一）拍摄的，我们的目标是实时对序列中每个图像的焦距和外部摄像机参数自动计算，而无需使用相机的位置和方向的先验知识。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度体系结构结合了对图像中特定关键的精确定位和鲁棒识别。我们的算法利用了野外线和玩家的图像位置，假设其地面位置要给出，以实现超出当前最新状态的准确性和鲁棒性。我们将展示其对挑战足球，篮球和排球基准数据集的有效性。
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.
