Real-Time Camera Pose Estimation for Sports Fields
This enables automated camera calibration for sports analysis and broadcasting, but it is incremental as it builds on existing keypoint-based methods.
The paper tackles the problem of real-time camera pose estimation for sports fields from uncalibrated video sequences, achieving state-of-the-art accuracy and robustness by combining field lines and player positions without prior knowledge.
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.