Automating sports broadcasting using ultra-high definition cameras, neural networks, and classical denoising
Sophia Rosney, Ciarán Donegan, Meegan Gower, Wissam A. Jassim, Hugh Denman, Donal Scannell, Anil C. Kokaram · 2022
Algorithms for automated sports broadcasting have been explored since the early 2000s. Systems consider automated control of virtual or physical cameras. However, output picture quality is compromised and capturing “off-the-ball” action remains a challenge for automated systems. In this paper, we present an exploration of the components of a semi-automated, high-quality broadcasting system. We simulate multiple dynamic views from fixed wide-angle cameras using an object detection network adapted to UHD wide-angle content. These views can be selected at the discretion of the director for broadcast. The final selected view then undergoes an enhancement process to address the optical blur and geometric distortion in wide-angle footage. Our overall system, through the combination of a human operator with automated view simulation algorithms, is capable of addressing many of the issues facing fully automated production in the complex environment of sport.