Implementing AI-powered semantic character recognition in motor racing sports
This addresses the problem for TV producers by reducing manual effort and enabling more dynamic overlays, though it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the labor-intensive process of manually overlaying contextual information about drivers in motor racing broadcasts by introducing an AI-powered system that automates driver tracking and enables dynamic overlays, with a deployed implementation used during live Formula E races.
Oftentimes TV producers of motor-racing programs overlay visual and textual media to provide on-screen context about drivers, such as a driver's name, position or photo. Typically this is accomplished by a human producer who visually identifies the drivers on screen, manually toggling the contextual media associated to each one and coordinating with cameramen and other TV producers to keep the racer in the shot while the contextual media is on screen. This labor-intensive and highly dedicated process is mostly suited to static overlays and makes it difficult to overlay contextual information about many drivers at the same time in short shots. This paper presents a system that largely automates these tasks and enables dynamic overlays using deep learning to track the drivers as they move on screen, without human intervention. This system is not merely theoretical, but an implementation has already been deployed during live races by a TV production company at Formula E races. We present the challenges faced during the implementation and discuss the implications. Additionally, we cover future applications and roadmap of this new technological development.