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.
@article{arxiv.2407.16076,
title = {PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips},
author = {Håkon Maric Solberg and Mehdi Houshmand Sarkhoosh and Sushant Gautam and Saeed Shafiee Sabet and Pål Halvorsen and Cise Midoglu},
journal= {arXiv preprint arXiv:2407.16076},
year = {2025}
}