PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips
This addresses the need for efficient sports analytics tools for soccer analysts and broadcasters, though it is incremental as it combines existing AI technologies.
The paper tackled the problem of automating player tracking and identification in soccer videos to generate player-specific highlight clips, resulting in a framework that accurately identifies teams and players from game footage, significantly reducing manual labor.
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.