在赛车运动中实现AI驱动的语义字符识别
Implementing AI-powered semantic character recognition in motor racing sports
摘要：
赛车节目的电视制作人通常会将视觉和文本媒体叠加在一起，以在屏幕上提供有关驾驶员的上下文，例如驾驶员的姓名、位置或照片。通常，这是由人工制作人完成的，人工制作人在屏幕上视觉识别驾驶员，手动切换与每个驾驶员相关联的上下文媒体，并与摄像师和其他电视制作人协调，以在上下文媒体在屏幕上的同时保持赛车手在镜头中。这种劳动密集型和高度专用的过程最适合静态叠加，并且难以在短镜头中同时叠加关于许多驱动程序的上下文信息。本文提出了一种系统，该系统在很大程度上自动化了这些任务，并使用深度学习实现动态覆盖，以跟踪驾驶员在屏幕上移动，而无需人工干预。该系统不仅是理论上的，而且已经由一家电视制作公司在Formula E比赛的现场比赛中部署了一个实现。我们提出了在实施过程中面临的挑战，并讨论了影响。此外，我们还介绍了这项新技术的未来应用和路线图。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
