Demo: Creating Player-Specific Soccer Highlight Clips with PlayerTV
This paper demonstrates PlayerTV, an innovative framework which harnesses state-of-the-art Artificial Intelligence (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. We present how PlayerTV can be run standalone as a core pipeline, as well as through an interactive Graphical User Interface (GUI).
Authors
- Saeed Shafiee Sabet (ORCID: https://orcid.org/0000-0001-5348-8546)
- H. Solberg (ORCID: https://orcid.org/0009-0003-6188-4823)
- Cise Midoglu (ORCID: https://orcid.org/0000-0003-0991-4418)
- Pål Halvorsen (ORCID: https://orcid.org/0000-0003-2073-7029)
- Sushant Gautam (ORCID: https://orcid.org/0000-0001-9232-2661)
- Mehdi Houshmand Sarkhoosh (ORCID: https://orcid.org/0009-0008-4616-4592)
Institutions
- OsloMet – Oslo Metropolitan University (NO)
- University of Oslo (NO)
- ForzaSys (Norway) (NO)
Publication Details
- Published
- 2024-09-18
- DOI
- https://doi.org/10.1109/cbmi62980.2024.10859224
- Citations
- 1
- Primary Topic
- Multimedia Communication and Technology
- Type
- article
- Field-Weighted Citation Impact
- 1.13
Funders
- Norges Forskningsråd