A Two-point Method for PTZ Camera Calibration in Sports
This addresses the problem of efficient camera calibration for sports analytics, but it is incremental as it builds on prior knowledge of camera base location and orientation.
The paper tackles the challenge of calibrating narrow field of view soccer cameras by proposing a two-point method that requires only two point correspondences, achieving superior performance over state-of-the-art methods on synthetic and real soccer datasets.
Calibrating narrow field of view soccer cameras is challenging because there are very few field markings in the image. Unlike previous solutions, we propose a two-point method, which requires only two point correspondences given the prior knowledge of base location and orientation of a pan-tilt-zoom (PTZ) camera. We deploy this new calibration method to annotate pan-tilt-zoom data from soccer videos. The collected data are used as references for new images. We also propose a fast random forest method to predict pan-tilt angles without image-to-image feature matching, leading to an efficient calibration method for new images. We demonstrate our system on synthetic data and two real soccer datasets. Our two-point approach achieves superior performance over the state-of-the-art method.