Implementing AI-powered Semantic Character Recognition in Motor Racing Sports

Oftentimes, TV producers overlay visual and textual media to provide context about racers appearing on screen, such as name, position and face shot. Typically, this is accomplished by a human producer visually identifying the racers on screen, manually toggling the contextual media associated to each one and coordinating with cameramen and other TV producers to keep the racer on shot while the contextual media is on screen. This labor-intensive process is mostly suited to static overlays and makes it difficult to overlay contextual information about many racers at the same time.

This paper presents a system that largely automates these tasks and enables dynamic overlays that uses deep learning to automatically track the racers as they move on screen. This system is not merely theoretical, an implementation has already been deployed to live TV production for Formula E broadcasts.? We will present the challenges found and solved in the implementation of this system, and we will discuss the implications and planned future applications of this new technological development.

Jose David Fern?ndez Rodr?guez | Virtually Live | M?laga, Spain
David Daniel Albarrac?n Molina | Virtually Live | M?laga, Spain
Jes?s Hormigo Cebolla | Virtually Live | M?laga, Spain