In the rapidly evolving field of sports analytics, the automation of targeted video processing is a pivotal advancement. We propose PlayerTV, an innovative framework which harnesses state-of-the-art AI technologies for automatic player tracking and identification in soccer videos. By integrating object detection and tracking, Optical Character Recognition (OCR), and color analysis, PlayerTV facilitates the generation of player-specific highlight clips from extensive game footage, significantly reducing the manual labor traditionally associated with such tasks. Preliminary results from the evaluation of our core pipeline, tested on a dataset from the Norwegian Eliteserien league, indicate that PlayerTV can accurately and efficiently identify teams and players, and our interactive Graphical User Interface (GUI) serves as a user-friendly application wrapping this functionality for streamlined use.

翻译：在快速发展的体育分析领域，针对性视频处理的自动化是一项关键进展。本文提出PlayerTV，一个创新的框架，它利用最先进的人工智能技术，实现对足球视频中球员的自动追踪与识别。通过集成目标检测与追踪、光学字符识别（OCR）以及色彩分析，PlayerTV能够从大量的比赛录像中生成针对特定球员的集锦片段，从而显著减少了传统上与此类任务相关的手动工作量。在挪威足球超级联赛数据集上对我们核心流程进行评估的初步结果表明，PlayerTV能够准确高效地识别球队和球员，并且我们交互式的图形用户界面（GUI）作为封装此功能的用户友好型应用程序，实现了流程化的便捷使用。