PlayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips

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.

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Publication Details

Published
2024-12-11
DOI
https://doi.org/10.1109/ism63611.2024.00019
Citations
5
Primary Topic
Video Analysis and Summarization
Type
article
Field-Weighted Citation Impact
1.25

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article

PlayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips

Saeed Shafiee Sabet, H. Solberg, Cise Midoglu, Pål Halvorsen et al.
5 citations
Video Analysis and Summarization
1.25
article

PlayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips

Saeed Shafiee Sabet, H. Solberg, Cise Midoglu, Pål Halvorsen, Sushant Gautam, Mehdi Houshmand Sarkhoosh
article en
5 citations

Abstract

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.

University of Oslo (NO), 657 Oslo (NO), ForzaSys (Norway) (NO)
Norges Forskningsråd
Openalex Percentile: Top 18%
Video Analysis and Summarization
1.25
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